Systems and methods for image processing

ABSTRACT

A method may include obtaining an original image. The method may also include determining a plurality of decomposition coefficients of the original image by decomposing the original image. The method may also include determining at least one enhancement coefficient by performing enhancement to at least one of the plurality of decomposition coefficients using a coefficient enhancement model. The method may also include generating an enhanced image corresponding to the original image based on the at least one enhancement coefficient.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Division of U.S. patent application Ser. No.17/106,176 filed on Nov. 29, 2020, which is a Continuation ofInternational Application No. PCT/CN2019/089388 filed on May 30, 2019,which claims priority to Chinese Patent Application No. 201810538825.7filed on May 30, 2018, Chinese Patent Application No. 201810996464.0filed on Aug. 29, 2018, and Chinese Patent Application No.201811564078.0 filed on Dec. 20, 2018, the contents of each of which arehereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to image processing, and inparticular, to systems and methods for image segmentation and/or imageenhancement.

BACKGROUND

Medical imaging becomes more and more important in modern medicine. Incertain cases, imaging processing, such as imaging segmentation and/orimage enhancement, is performed on medical images to help doctors tomake a diagnosis. Therefore, it is desirable to provide systems andmethod to realize more accurate and more efficient image segmentationand/or image enhancement.

SUMMARY

According to a first aspect of the present disclosure, a system mayinclude one or more storage devices and one or more processorsconfigured to communicate with the one or more storage devices. The oneor more storage devices may include a set of instructions. When the oneor more processors execute the set of instructions, the one or moreprocessors may be directed to perform one or more of the followingoperations. The one or more processors may obtain a breast image of anobject that is acquired by an imaging device; determine a projectioncurve based on the breast image; determining a first valley point and asecond valley point of the projection curve; determine a peak point ofthe projection curve based the first valley point and the second valleypoint of the projection curve; determine a first valley location, asecond valley location, and a peak location in the breast image based onthe peak point, the first valley point, and the second valley point ofthe projection curve; and determine a breast region in the breast imagebased on the first valley location, the second valley location, and thepeak location.

In some embodiments, the determining the projection curve based on thebreast image includes: dividing the breast image into a body region anda background region, the body region including a breast; generating abinary image by designating pixel values of pixels in the body region as1 and designating pixel values of pixels in the background region as 0;determining a chest-wall side of the binary image; and obtaining theprojection curve by determining a plurality of sums each of which is asum of pixel values of a row of pixels in the binary image, the row ofpixels being arranged along a direction between the chest-wall side anda side of the binary image opposite to the chest-wall side.

In some embodiments, for each point on the projection curve, a firstcoordinate on a first coordinate axis of the point represents a positionof a row of pixels in the binary image, and a second coordinate on asecond coordinate axis of the point represents a sum of pixies values ofthe corresponding row of pixels.

In some embodiments, 4 the determining the first valley point and thesecond valley point of the projection curve includes: obtaining a presetdistance between the first coordinates of the first valley point and thesecond valley point, and preset positions of the first valley point andthe second valley point in the projection curve, the preset distance andthe preset positions being set based on breast characteristics;determining whether there is any valley point or point whose secondcoordinate is 0 in the projection curve; and determining the firstvalley point and the second valley point in the projection curve basedon the determination result, the preset distance, and the presetpositions.

In some embodiments, the determining the first valley location, thesecond valley location, and the peak location in the breast image basedon the peak point, the first valley point, and the second valley pointof the projection curve includes: determining that the second coordinateof the first valley point or the second valley point is greater than thesecond coordinate of the peak point; in response to a determination thatthe second coordinate of the first valley point or the second valleypoint is greater than the second coordinate of the peak point, rotatingthe binary image so that a straight line connecting points in therotated binary image corresponding to the first valley point and thesecond valley point is parallel to or coincides with the secondcoordinate axis; determining a new projection curve based on the rotatedbinary image; and updating the first valley point, the second valleypoint, and the peak point based on the new projection curve; anddetermining the first valley location, the second valley location, andthe peak location in the breast image based on the updated first valleypoint, the updated second valley point, and the updated peak point.

In some embodiments, the determining the first valley location, thesecond valley location, and the peak location in the breast image basedon the peak point, the first valley point, and the second valley pointof the projection curve includes: determining that there is no valleypoint in the projection curve and there is at least one point whosesecond coordinate is 0 on only one side of the peak point of theprojection curve; in response to a determination that there is no valleypoint in the projection curve and there is at least one point whosesecond coordinate is 0 on only one side of the peak point of theprojection curve, rotating the binary image so that a straight lineconnecting points in the rotated binary image corresponding to the firstvalley point and the second valley point is parallel to or coincideswith the second coordinate axis; determining a new projection curvebased on the rotated binary image; updating the first valley point, thesecond valley point, and the peak point based on the new projectioncurve; and determining the first valley location, the second valleylocation, and the peak location in the breast image based on the updatedfirst valley point, the updated second valley point, and the updatedpeak point.

In some embodiments, the determining the breast region in the breastimage based on the first valley location, the second valley location,and the peak location includes: determining a first straight line fromthe peak location, the first straight line being perpendicular to asecond straight line connecting the first valley location and the secondvalley location; determining an intersection of the first straight lineand a chest-wall side of the breast image; and determining the breastregion in the breast image by connecting the first valley location, thesecond valley location, and intersection.

According to another aspect of the present disclosure, a method mayinclude one or more of the following operations. One or more processorsmay obtain a breast image of an object that is acquired by an imagingdevice; determine a projection curve based on the breast image;determining a first valley point and a second valley point of theprojection curve; determine a peak point of the projection curve basedthe first valley point and the second valley point of the projectioncurve; determine a first valley location, a second valley location, anda peak location in the breast image based on the peak point, the firstvalley point, and the second valley point of the projection curve; anddetermine a breast region in the breast image based on the first valleylocation, the second valley location, and the peak location.

According to yet another aspect of the present disclosure, a system mayinclude one or more storage devices and one or more processorsconfigured to communicate with the one or more storage devices. The oneor more storage devices may include a set of instructions. When the oneor more processors execute the set of instructions, the one or moreprocessors may be directed to perform one or more of the followingoperations. The one or more processors may obtain a pre-exposure breastimage of a breast of an object that is acquired by an imaging device;determine a breast region in the pre-exposure breast image by processingthe pre-exposure breast image based on the method provided in thepresent disclosure; determine a gland region in the determined breastregion; determine a gray level of the gland region; obtain a presetrelationship of a pre-exposure X-ray dose used to acquire thepre-exposure breast image, a compression thickness of the breast, thegray level of the gland region, and AEC parameters; and determine theAEC parameters based on the preset relationship, the X-ray dose, thecompression thickness of the breast, and the gray level of the glandregion.

According to yet another aspect of the present disclosure, a system mayinclude one or more storage devices and one or more processorsconfigured to communicate with the one or more storage devices. The oneor more storage devices may include a set of instructions. When the oneor more processors execute the set of instructions, the one or moreprocessors may be directed to perform one or more of the followingoperations. The one or more processors may obtain a breast image of anobject that is acquired by an imaging device; obtain a binary templateincluding a direct exposure region of the breast image; obtain a binarygradient image by performing gradient transform and binarization to thebreast image, the binary gradient image including one or more straightline features; determine a preliminary region based on the binarytemplate and the binary gradient image; process at least one of thebreast image, the binary template, and the binary gradient image toreduce an effect of overexposure or tissue of the object with high X-rayattenuation in the breast image on the one or more straight linefeatures; identify the one or more straight line features in the binarygradient image based on the processing result; and determine an edge ofa collimator of the imaging device in the preliminary region based onthe identified one or more straight line features, the edge including atleast one of the identified one or more straight line features each ofwhich has a length longer than a length threshold and is out of thedirect exposure region.

In some embodiments, the identifying the one or more straight linefeatures in the binary gradient image includes: identify the one or morestraight line features in the binary gradient image by determining oneor more row projection values and one or more column projection values,each of the one or more row projection values being a sum of pixelvalues of a row of pixels in the binary gradient image, each of the oneor more column projection values being a sum of pixel values of a columnof pixels in the binary gradient image.

In some embodiments, the breast image includes a chest-wall side, a sideopposite to the chest-wall side, an upper side, and a lower side; thedetermined edge of the collimator in the breast image includes theidentified straight line features including a row of pixels associatedwith the upper side, a row of pixels associated with the lower side, anda column of pixels associated with the side opposite to the chest-wallside, the row projection value of the row of pixels associated with theupper side is a first projection value, the row projection value of therow of pixels associated with the lower side is a second projectionvalue, and the column projection value of the column of pixelsassociated with the side opposite to the chest-wall side is a thirdprojection value; and the length threshold includes a first lengththreshold, a second length threshold, and a third length threshold, thefirst projection value is greater than the first length threshold, thesecond projection value is greater than the second length threshold, andthe third projection value is greater than the third length threshold.

In some embodiments, the binary gradient image includes a firstsub-image, a second sub-image, a third sub-image, and a fourthsub-image; and the obtaining the binary gradient image by performinggradient transform and binarization to the breast image includes:processing the breast image by performing gradient transform andbinarization to the breast image; obtaining the first sub-image based ona first gradient threshold and the processing result, the firstsub-image representing a contour feature of the breast image; andobtaining the second sub-image associated with the upper side, the thirdsub-image associated with the lower side, and the third sub-imageassociated with the side opposite to the chest-wall side based on asecond gradient threshold and the processing result, the first gradientthreshold being greater than the second gradient threshold.

In some embodiments, the one or more processors may obtain a physicalposition of the collimator in the imaging device; project at least apart of the collimator on the breast image based on the physicalposition of the collimator; and determine the edge of the collimator inthe breast image based on the projection.

In some embodiments, the obtaining the breast image of the objectincludes: obtain an original breast image of the object that is acquiredby the imaging device; obtain a physical position of the collimator inthe imaging device; project the collimator on the original breast imagebased on the physical position of the collimator; and obtain the breastimage by cropping the original breast image along the projection of thecollimator.

In some embodiments, the processing at least one of the breast image,the binary template, and the binary gradient image to reduce the effectof the overexposure or the tissue of the object with high X-rayattenuation in the breast image on the one or more straight linefeatures includes: add a make-up part to at least one of the one or morestraight line features in the preliminary region, the make-up part beingin a region corresponding to tissue of the object with high X-rayattenuation.

In some embodiments, the adding the make-up part to the at least one ofthe one or more straight line features in the preliminary regionincludes: obtain a first low-gray template based on the breast image anda first gray threshold, the first low-gray template including a firstregion representing the tissue of the object with high X-rayattenuation; determine a second region by performing dilation to thefirst region using a bilation kernel to generate a second low-graytemplate, the second region being larger than the first region; obtain athird low-gray template by removing a region other than the preliminaryregion from the second low-gray template, the third low-gray templateincluding a third region corresponding to the second region, the thirdregion being smaller than the second region; and add the make-up part tothe at least one of the one or more straight line features in thepreliminary region by extending the at least one of the one or morestraight line features to the third region.

In some embodiments, the processing at least one of the breast image,the binary template, and the binary gradient image to reduce the effectof the overexposure or the tissue of the object with high X-rayattenuation in the breast image on the one or more straight linefeatures includes: perform erosion to the direct exposure region in thebinary template using a first erosion kernel or a second erosion kernel,a size of the second erosion kernel being larger than that of the firsterosion kernel.

In some embodiments, the performing the erosion to the direct exposureregion in the binary template using the first erosion kernel or thesecond erosion kernel includes: obtain a high-gray template based on thebreast image and a second gray threshold, the high-gray templateincluding a first gray region in which gray values of pixels are greaterthan or equal to the second gray threshold; determine whether a ratio ofa size of the first gray region in the high-gray template to a size ofthe direct exposure region in the binary template is greater than aratio threshold; perform the erosion to the direct exposure region inthe binary template based on a determination result.

In some embodiments, the determination result includes that the ratio ofthe size of the first gray region to the size of the direct exposureregion is greater than the ratio threshold, and the performing theerosion to the direct exposure region in the binary template based on adetermination result includes performing the erosion to the directexposure region in the binary template using the second erosion kernel.

In some embodiments, the determination result includes that the ratio ofthe size of the first gray region to the size of the direct exposureregion is less than the ratio threshold, and the performing the erosionto the direct exposure region in the binary template based on adetermination result includes performing the erosion to the directexposure region in the binary template using the first erosion kernel orperforming no erosion to the direct exposure region.

According to yet another aspect of the present disclosure, a method mayinclude one or more of the following operations. One or more processorsmay obtain a breast image of an object that is acquired by an imagingdevice; obtain a binary template including a direct exposure region ofthe breast image; obtain a binary gradient image by performing gradienttransform and binarization to the breast image, the binary gradientimage including one or more straight line features; determine apreliminary region based on the binary template and the binary gradientimage; process at least one of the breast image, the binary template,and the binary gradient image to reduce an effect of overexposure ortissue of the object with high X-ray attenuation in the breast image onthe one or more straight line features; identify the one or morestraight line features in the binary gradient image based on theprocessing result; and determine an edge of a collimator of the imagingdevice in the preliminary region based on the identified one or morestraight line features, the edge including at least one of theidentified one or more straight line features each of which has a lengthlonger than a length threshold and is out of the direct exposure region.

According to yet another aspect of the present disclosure, a method mayinclude one or more of the following operations. One or more processorsmay obtain an original image; obtain a plurality of decompositioncoefficients of the original image by decomposing the original image;obtain at least one enhancement coefficient by performing enhancement toat least one of the plurality of decomposition coefficients using amachine learning model; and obtain an enhanced image corresponding tothe original image based on the at least one enhancement coefficient.

According to yet another aspect of the present disclosure, a system mayinclude one or more storage devices and one or more processorsconfigured to communicate with the one or more storage devices. The oneor more storage devices may include a set of instructions. When the oneor more processors execute the set of instructions, the one or moreprocessors may be directed to perform one or more of the followingoperations. The one or more processors may obtain an original image;obtain a plurality of decomposition coefficients of the original imageby decomposing the original image; obtain at least one enhancementcoefficient by performing enhancement to at least one of the pluralityof decomposition coefficients using a machine learning model; and obtainan enhanced image corresponding to the original image based on the atleast one enhancement coefficient.

According to yet another aspect of the present disclosure, anon-transitory computer readable medium may comprise at least one set ofinstructions. The at least one set of instructions may be executed byone or more processors of a computing device. The one or more processorsmay obtain an original image; obtain a plurality of decompositioncoefficients of the original image by decomposing the original image;obtain at least one enhancement coefficient by performing enhancement toat least one of the plurality of decomposition coefficients using amachine learning model; and obtain an enhanced image corresponding tothe original image based on the at least one enhancement coefficient.

In some embodiments, the coefficient enhancement model may be obtainedby performing operations including: obtaining a training set including aplurality of sample pairs, each of the plurality of sample pairsincluding a plurality of first decomposition coefficients of a sampleimage and a plurality of second decomposition coefficients correspondingto the first decomposition coefficients; and obtaining the coefficientenhancement model by training, based on the training set, a preliminarymodel.

In some embodiments, obtaining the training set including the pluralityof sample pairs may include: obtaining an enhanced image correspondingto the sample image by performing image processing on the sample image;obtaining the plurality of first decomposition coefficients of thesample image and the plurality of second decomposition coefficients ofthe enhanced image corresponding to the sample image by decomposing thesample image and the corresponding enhanced image; and determining theplurality of first decomposition coefficients and the plurality ofsecond decomposition coefficients as the sample pair.

In some embodiments, the image processing may include at least one of ahistogram equalization algorithm, a gamma conversion algorithm, anexponential image enhancement algorithm, or a logarithmic imageenhancement algorithm.

In some embodiments, obtaining the training set including the pluralityof sample pairs may include: obtaining the plurality of firstdecomposition coefficients of the sample image by decomposing the sampleimage; obtaining the plurality of second decomposition coefficients byperforming enhancement to the plurality of first decompositioncoefficients of the sample image; and determining the plurality of firstdecomposition coefficients of the sample image and the plurality ofsecond decomposition coefficients as the sample pair.

In some embodiments, the coefficient enhancement model may include atrained deep learning model.

In some embodiments, the plurality of decomposition coefficients of theoriginal image may be obtained by decomposing the original image using amulti-resolution analysis algorithm.

In some embodiments, the multi-resolution analysis algorithm may includeat least one of a Gauss-Laplace pyramid decomposition algorithm or awavelet decomposition algorithm.

In some embodiments, the at least one processor may perform apre-processing operation on the original image.

In some embodiments, performing the pre-processing operation on theoriginal image may include: obtaining a pre-processed image by adjustingone or more display parameters of the original image.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device according to some embodimentsof the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device according to some embodiments ofthe present disclosure;

FIG. 4 is a schematic block diagram illustrating an exemplary processingdevice according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for determininga breast region in a breast image according to some embodiments of thepresent disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for determininga projection curve according to some embodiments of the presentdisclosure;

FIG. 7 is a schematic diagram illustrating an exemplary breast imageaccording to some embodiments of the present disclosure;

FIG. 8 is a schematic diagram illustrating an exemplary image obtainedby performing edge detection according to some embodiments of thepresent disclosure;

FIG. 9 is a schematic diagram illustrating an exemplary binary imageaccording to some embodiments of the present disclosure;

FIG. 10 is a schematic diagram illustrating an exemplary projectioncurve according to some embodiments of the present disclosure;

FIG. 11 is a flowchart illustrating an exemplary process for determininga body region and a background region according to some embodiments ofthe present disclosure;

FIG. 12A is a flowchart illustrating an exemplary process fordetermining a first valley point and a second valley point of aprojection curve according to some embodiments of the presentdisclosure;

FIGS. 12B-12C are schematic diagrams illustrating exemplary breastimages showing a lateral view of a breast according to some embodimentsof the present disclosure;

FIG. 13 is a flowchart illustrating an exemplary process for determininga first valley location, a second valley location, and a peak locationaccording to some embodiments of the present disclosure;

FIG. 14 is a schematic diagram illustrating an exemplary breast imageshowing a lateral view of a breast according to some embodiments of thepresent disclosure;

FIG. 15 is a schematic diagram illustrating an exemplary breast imagecorresponding to a rotated binary image according to some embodiments ofthe present disclosure;

FIG. 16 is a flowchart illustrating an exemplary process for determininga first valley location, a second valley location, and a peak locationaccording to some embodiments of the present disclosure;

FIG. 17 is a schematic diagram illustrating an exemplary breast imageshowing a lateral view of a breast according to some embodiments of thepresent disclosure;

FIG. 18 is a flowchart illustrating an exemplary process for determininga breast region according to some embodiments of the present disclosure;

FIG. 19 is a schematic diagram illustrating an exemplary breast regionin a breast image according to some embodiments of the presentdisclosure;

FIG. 20 is a flowchart illustrating an exemplary process for determininga gland region according to some embodiments of the present disclosure;

FIG. 21 is a schematic diagram illustrating an exemplary gray histogramof a breast region in a breast image according to some embodiments ofthe present disclosure;

FIG. 22 is a schematic diagram illustrating an exemplary gland region ina breast image according to some embodiments of the present disclosure;

FIG. 23 is a flowchart illustrating an exemplary process for determiningAEC parameters of an imaging device according to some embodiments of thepresent disclosure;

FIG. 24 is a schematic block diagram illustrating an exemplaryprocessing device according to some embodiments of the presentdisclosure;

FIG. 25 is a flowchart illustrating an exemplary process for imageenhancement according to some embodiments of the present disclosure;

FIG. 26 is a flowchart illustrating an exemplary process for obtaining atraining set according to some embodiments of the present disclosure;

FIG. 27 is a flowchart illustrating an exemplary process for obtaining atraining set according to some embodiments of the present disclosure;

FIG. 28 is a schematic diagram of an exemplary Gauss-pyramiddecomposition algorithm according to some embodiments of the presentdisclosure;

FIG. 29 is a schematic diagram of an exemplary wavelet decompositionalgorithm according to some embodiments of the present disclosure;

FIG. 30 is a schematic block diagram illustrating an exemplaryprocessing device according to some embodiments of the presentdisclosure;

FIG. 31A is a flowchart illustrating an exemplary process fordetermining an edge of a collimator of an imaging device in a breastimage according to some embodiments of the present disclosure;

FIG. 31B is a schematic block diagram illustrating an exemplary breastimage with a bad line according to some embodiments of the presentdisclosure;

FIG. 31C is a schematic block diagram illustrating an exemplarypre-processed breast image without a bad line according to someembodiments of the present disclosure;

FIG. 32A is a schematic diagram showing an exemplary edge of acollimator according to some embodiments of the present disclosure;

FIG. 32B is a schematic diagram of an exemplary gray value feature curverelated to the edge of the collimator in FIG. 32A according to someembodiments of the present disclosure;

FIG. 33A is a schematic diagram of an exemplary pre-processed breastimage according to some embodiments of the present disclosure;

FIGS. 33B-33E are schematic diagrams of exemplary first sub-image,second sub-image, third sub-image, fourth sub-image, respectively,according to some embodiments of the present disclosure;

FIG. 34A is a schematic diagram of an exemplary pre-processed breastimage according to some embodiments of the present disclosure;

FIG. 34B is a schematic diagram of an exemplary first sub-imageaccording to some embodiments of the present disclosure;

FIG. 34C is a schematic diagram of an exemplary binary templateaccording to some embodiments of the present disclosure;

FIG. 34D is a schematic diagram of an exemplary preliminary regionaccording to some embodiments of the present disclosure;

FIG. 35A is a schematic diagram of an exemplary pre-processed breastimage according to some embodiments of the present disclosure;

FIG. 35B is a schematic diagram of an exemplary first sub-imageaccording to some embodiments of the present disclosure;

FIG. 35C is a schematic diagram of an exemplary binary templateaccording to some embodiments of the present disclosure;

FIG. 35D is a schematic diagram of an exemplary preliminary regionaccording to some embodiments of the present disclosure;

FIG. 36A is a schematic diagram of an exemplary breast image includingoverexposure according to some embodiments of the present disclosure;

FIG. 36B is a schematic diagram of an exemplary binary templateaccording to some embodiments of the present disclosure;

FIG. 36C is a schematic diagram of an exemplary high gray templateaccording to some embodiments of the present disclosure;

FIG. 37A is a schematic diagram of an exemplary breast image including alow gray region according to some embodiments of the present disclosure;

FIG. 37B is a schematic diagram of an exemplary third sub-imageaccording to some embodiments of the present disclosure;

FIGS. 37C-37E are schematic diagrams of exemplary low gray templatesaccording to some embodiments of the present disclosure;

FIG. 37F is a schematic diagram of an exemplary make-up part of astraight line feature according to some embodiments of the presentdisclosure; and.

FIG. 38 is a flowchart illustrating an exemplary process for determiningan edge of a collimator of an imaging device in a breast image accordingto some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “unit,” “module,” and/or“block” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by another expression if theyachieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2 ) may beprovided on a computer readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedof connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and components for medical imaging and/ormedical treatment. In some embodiments, the medical system may includean imaging system. The imaging system may include a single modalityimaging system and/or a multi-modality imaging system. The singlemodality imaging system may include, for example, an X-ray imagingsystem (e.g, a computed tomography (CT) imaging system, a digitalsubtraction angiography (DSA) imaging system, a digital radiology (DR)imaging system, a computed radiology (CR) imaging system, etc.), anultrasound imaging system (e.g., a color Doppler flow imaging (CDFI)system), a magnetic resonance imaging (MRI) system, or a nuclear medicalimaging system (e.g., a positron emission tomography (PET) imagingsystem, a single photon emission computed tomography (SPECT) imagingsystem, etc.). The multi-modality imaging system may include, forexample, a computed tomography-magnetic resonance imaging (MRI-CT)system, a positron emission tomography-magnetic resonance imaging(PET-MRI) system, a single photon emission computed tomography-magneticresonance imaging (SPECT-MRI) system, a digital subtractionangiography-magnetic resonance imaging (DSA-MRI) system, a positronemission tomography-magnetic resonance imaging-computed tomography(PET-CT) imaging system, etc. In some embodiments, the medical systemmay include a treatment system. The treatment system may include atreatment plan system (TPS), image-guide radiotherapy (IGRT), etc. Theimage-guide radiotherapy (IGRT) may include a treatment device and animaging device. The treatment device may include a linear accelerator, acyclotron, a synchrotron, etc., configured to perform a radio therapy ona subject. The treatment device may include an accelerator of species ofparticles including, for example, photons, electrons, protons, or heavyions. The imaging device may include an MRI scanner, a CT scanner (e.g.,cone beam computed tomography (CBCT) scanner), a digital radiology (DR)scanner, an electronic portal imaging device (EPID), etc.

In some embodiments, the systems provided herein may be used for medicaldiagnosis, for example, red blood cell and white blood cell differentialdiagnosis, chromosome analysis, cancer cell recognition diagnosis, boneand joint soft tissue diagnosis, intracerebral hematoma, extracerebralhematoma, brain tumors, intracranial aneurysms, arteriovenousmalformations, cerebral ischemia, intraspinal tumors, syringomyelia andhydrocephalus diagnosis, lumbar disc herniation, diagnosis of primaryliver cancer, etc. In some embodiments, the systems provided herein mayalso be used for scenarios other than a medical diagnosis. For example,image enhancement in natural disaster prediction and forecasting in thefield of remote sensing, environmental pollution monitoring,meteorological satellite cloud image processing, identification ofground military targets, and image recognition in security systems.

It should be noted that, in the present disclosure, an image, or aportion thereof (e.g., a region in the image) corresponding to an object(e.g., tissue, an organ, a tumor, etc.) may be referred to as an image,or a portion of thereof (e.g., a region) of or including the object, orthe object itself. For instance, a region in an image that correspondsto or represents a breast may be described as that the region includes abreast. As another example, an image of or including a breast may bereferred to a breast image, or simply breast. For brevity, that aportion of an image corresponding to or representing an object isprocessed (e.g., extracted, segmented, etc.) may be described as theobject is processed. For instance, that a portion of an imagecorresponding to a breast is segmented from the rest of the image may bedescribed as that the breast is segmented from the image.

FIG. 1 is a schematic diagram illustrating an exemplary imaging system100 according to some embodiments of the present disclosure. Asillustrated, the imaging system 100 may include an imaging device 110, anetwork 120, a terminal 130, a processing device 140, and a storagedevice 150. The components of the imaging system 100 may be connected inone or more of various ways. Mere by way of example, as illustrated inFIG. 1 , the imaging device 110 may be connected to the processingdevice 140 through the network 120. As another example, the imagingdevice 110 may be connected to the processing device 140 directly (asindicated by the bi-directional arrow in dotted lines linking theimaging device 110 and the processing device 140). As a further example,the storage device 150 may be connected to the processing device 140directly or through the network 120. As still a further example, aterminal device (e.g., 131, 132, 133, etc.) may be connected to theprocessing device 140 directly (as indicated by the bi-directional arrowin dotted lines linking the terminal 130 and the processing device 140)or through the network 120.

In some embodiments, the imaging device 110 may be an image or videocapture/acquiring device. In some embodiments, the imaging device 110may include a medical imaging device, a camera, a laptop computer, anin-vehicle built-in device, a mobile device, etc., or any combinationthereof. In some embodiments, the camera may include a surveillancecamera used in a supermarket, a mall, a home, an office area, or thelike, or any combination thereof. In some embodiments, the in-vehiclebuilt-in device may include a laptop computer, a head up display (HUD),an on-board diagnostic (OBD) system, a driving recorder, a carnavigation, or the like, or any combination thereof. In someembodiments, the mobile device may include a smartphone, a personaldigital assistant (PDA), a tablet computer, a handheld game player, asmart glasses, a smart watch, a wearable device, a virtual reality, adisplay enhancement device, or the like, or any combination thereof.

If the imaging device 110 is the medical imaging device, the imagingdevice 110 may be used to scan an object located within its detectionregion and generate a plurality of scan data (e.g., digital signals)used to generate one or more images relating to the object. In thepresent disclosure, “subject” and “object” are used interchangeably.Mere by way of example, the object may include a patient, a man-madeobject, etc. As another example, the object may include a specificportion, organ, and/or tissue of a patient. For example, the object mayinclude head, brain, neck, body, shoulder, arm, thorax, cardiac,stomach, blood vessel, soft tissue, knee, feet, or the like, or anycombination thereof.

In some embodiments, the imaging device 110 may be used to scan a breastof the object (e.g., a patient). For example, the imaging device 110 maybe an X-ray device or an ultrasound device. Taking the X-ray device asan example, the X-ray device may include a breast-holder tray on whichthe patient lays her breast, an X-ray tube, and a detector. Thebreast-holder tray may be placed on the top of the detector. Thedetector may be placed beneath the breast-holder tray. The X-ray tubemay emit X-rays going through the breast. The detector may be locatedopposite to the X-ray tube so as to detect the X-rays that have crossedthe patient's breast and the breast-holder tray. The detector maytransform the light signals of the detected X-rays into digital signalsand transmit the digital signals to the processing device 140 forfurther processing (e.g., generating a breast image). In someembodiments, the X-ray device may further include a compression pad. Forreasons related both to the immobilizing of the breast and to imagequality or intensity of X-rays delivered to the patient's breast, it isnecessary to compress the patient's breast during the scan process. Thecompression force may be applied through the compression pad thatcompresses the breast on the breast-holder tray. In some embodiments,the X-ray device may further include a high-voltage generator configuredto provide the voltage that is required for the X-ray tube to produceX-rays. In some embodiments, the X-ray device may further include acollimator configured to adjust an X-ray irradiation range. Thecollimator also can absorb some scattered X-rays, which may improve theimage quality. The collimator may be located in front of the X-ray tubein the emitting direction of the X-rays.

The network 120 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging system 100 (e.g., theimaging device 110, the terminal 130, the processing device 140, or thestorage device 150) may communicate information and/or data with one ormore other components of the imaging system 100 via the network 120. Forexample, the processing device 140 may obtain scan data (e.g., digitalsignals) of a breast of an object (e.g., a patient) from the imagingdevice 110 via the network 120. In some embodiments, the network 120 maybe any type of wired or wireless network, or a combination thereof. Thenetwork 120 may be and/or include a public network (e.g., the Internet),a private network (e.g., a local area network (LAN), a wide area network(WAN)), etc.), a wired network (e.g., an Ethernet network), a wirelessnetwork (e.g., an 802.11 network, a Wi-Fi network, etc.), a cellularnetwork (e.g., a Long Term Evolution (LTE) network), a frame relaynetwork, a virtual private network (“VPN”), a satellite network, atelephone network, routers, hubs, switches, server computers, and/or anycombination thereof. Merely by way of example, the network 120 mayinclude a cable network, a wireline network, a fiber-optic network, atelecommunications network, an intranet, a wireless local area network(WLAN), a metropolitan area network (MAN), a public telephone switchednetwork (PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or any combination thereof. Insome embodiments, the network 120 may include one or more network accesspoints. For example, the network 120 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the imaging system 100may be connected to the network 120 to exchange data and/or information.

The terminal 130 include a mobile device 131, a tablet computer 132, alaptop computer 133, or the like, or any combination thereof. In someembodiments, the mobile device 131 may include a smart home device, awearable device, a smart mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a smart bracelet, smartfootgear, a pair of smart glasses, a smart helmet, a smart watch, smartclothing, a smart backpack, a smart accessory, or the like, or anycombination thereof. In some embodiments, the smart mobile device mayinclude a smartphone, a personal digital assistant (PDA), a gamingdevice, a navigation device, a point of sale (POS) device, or the like,or any combination thereof. In some embodiments, the virtual realitydevice and/or the augmented reality device may include a virtual realityhelmet, a virtual reality glass, a virtual reality patch, an augmentedreality helmet, an augmented reality glass, an augmented reality patch,or the like, or any combination thereof. For example, the virtualreality device and/or the augmented reality device may include a Google™Glass, an Oculus Rift, a Hololens, a Gear VR, etc. In some embodiments,the terminal 130 may remotely operate the imaging device 110 and/or theprocessing device 140. In some embodiments, the terminal 130 may operatethe imaging device 110 and/or the processing device 140 via a wirelessconnection. In some embodiments, the terminal 130 may receiveinformation and/or instructions inputted by a user, and send thereceived information and/or instructions to the imaging device 110 or tothe processing device 140 via the network 120. In some embodiments, theterminal 130 may receive data and/or information from the processingdevice 140. In some embodiments, the terminal 130 may be part of theprocessing device 140. In some embodiments, the terminal 130 may beomitted.

The processing device 140 may process data and/or information obtainedfrom the imaging device 110, the terminal 130, and/or the storage device150. For example, the processing device 140 may generate one or moremedical images (e.g., breast images) by processing scan data (e.g.,digital signals) from the imaging device 110. As another example, theprocessing device 140 may determine a breast region in a breast image.As still another example, the processing device 140 may determineautomatic exposure control (AEC) parameters for scanning a breast usingthe imaging device 110 based on the determined breast region. As stillanother example, the processing device 140 may determine a collimatorregion in a breast image. As still another example, the processingdevice 140 may perform image enhancement to an image. In someembodiments, the processing device 140 may be a single server, or aserver group. The server group may be centralized or distributed. Insome embodiments, the processing device 140 may be local or remote. Forexample, the processing device 140 may access information and/or datastored in or acquired by the imaging device 110, the terminal 130,and/or the storage device 150 via the network 120. As another example,the processing device 140 may be directly connected to the imagingdevice 110 (as illustrated by the bidirectional arrow in dashed linesconnecting the processing device 140 and the imaging device 110 in FIG.1 ), the terminal 130 (as illustrated by the bidirectional arrow indashed lines connecting the processing device 140 and the terminal 130in FIG. 1 ), and/or the storage device 150 to access stored or acquiredinformation and/or data. In some embodiments, the processing device 140may be implemented on a cloud platform. Merely by way of example, thecloud platform may include a private cloud, a public cloud, a hybridcloud, a community cloud, a distributed cloud, an inter-cloud, amulti-cloud, or the like, or any combination thereof. In someembodiments, the processing device 140 may be implemented on a computingdevice 200 having one or more components illustrated in FIG. 2 in thepresent disclosure.

The storage device 150 may store data and/or instructions. In someembodiments, the storage device 150 may store data obtained from theimaging device 110, the terminal 130 and/or the processing device 140.For example, the storage device 150 may store medical images (e.g.,breast images) generated by the processing device 140. In someembodiments, the storage device 150 may store data and/or instructionsthat the processing device 140 may execute or use to perform exemplarymethods described in the present disclosure. For example, the storagedevice 150 may store instructions that the processing device 140 mayexecute to perform operations including at least one of: generating oneor more medical images (e.g., breast images), determining a breastregion in a breast image, determining automatic exposure control (AEC)parameters for scanning a breast using the imaging device 110 based onthe determined breast region, determining a collimator region in abreast image, and performing image enhancement to an image. In someembodiments, the storage device 150 may include a mass storage device, aremovable storage device, a volatile read-and-write memory, a read-onlymemory (ROM), or the like, or any combination thereof. Exemplary massstorage may include a magnetic disk, an optical disk, a solid-statedrive, etc. Exemplary removable storage may include a flash drive, afloppy disk, an optical disk, a memory card, a zip disk, a magnetictape, etc. Exemplary volatile read-and-write memory may include a randomaccess memory (RAM). Exemplary RAM may include a dynamic RAM (DRAM), adouble date rate synchronous dynamic RAM (DDR SDRAM), a static RAM(SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc.Exemplary ROM may include a mask ROM (MROM), a programmable ROM (PROM),an erasable programmable ROM (PEROM), an electrically erasableprogrammable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digitalversatile disk ROM, etc. In some embodiments, the storage device 150 maybe implemented on a cloud platform. Merely by way of example, the cloudplatform may include a private cloud, a public cloud, a hybrid cloud, acommunity cloud, a distributed cloud, an inter-cloud, a multi-cloud, orthe like, or any combination thereof.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more components of the imagingsystem 100 (e.g., the imaging device 110, the processing device 140, theterminal 130, etc.). One or more components of the imaging system 100may access the data or instructions stored in the storage device 150 viathe network 120. In some embodiments, the storage device 150 may bedirectly connected to or communicate with one or more components of theimaging system 100 (e.g., the imaging device 110, the processing device140, the terminal 130, etc.). In some embodiments, the storage device150 may be part of the processing device 140.

In some embodiments, the imaging system 100 may further include one ormore power supplies (not shown in FIG. 1 ) connected to one or morecomponents of the imaging system 100 (e.g., the imaging device 110, theprocessing device 140, the terminal 130, the storage device 150, etc.).

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device on which the processing device140 may be implemented according to some embodiments of the presentdisclosure. As illustrated in FIG. 3 , the computing device 200 mayinclude a processor 210, a storage 220, an input/output (I/O) 230, and acommunication port 240.

The processor 210 may execute computer instructions (program code) andperform functions of the processing device 140 in accordance withtechniques described herein. The computer instructions may includeroutines, programs, objects, components, signals, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may generate one ormore medical images (e.g., breast images) by processing scan data (e.g.,digital signals) from the imaging device 110. As another example, theprocessor 210 may determine a breast region in a breast image. As stillanother example, the processor 210 may determine automatic exposurecontrol (AEC) parameters for scanning a breast using the imaging device110 based on the determined breast region. As still another example, theprocessor 210 may determine a collimator region in a breast image. Asstill another example, the processor 210 may perform image enhancementto an image. In some embodiments, the processor 210 may include amicrocontroller, a microprocessor, a reduced instruction set computer(RISC), an application specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof.

Merely for illustration purposes, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, and thus operations of a method that are performed by oneprocessor as described in the present disclosure may also be jointly orseparately performed by the multiple processors. For example, if in thepresent disclosure the processor of the computing device 200 executesboth operations A and B, it should be understood that operations A andstep B may also be performed by two different processors jointly orseparately in the computing device 200 (e.g., a first processor executesoperation A and a second processor executes operation B, or the firstand second processors jointly execute operations A and B).

The storage 220 may store data/information obtained from the imagingdevice 110, the terminal 130, the storage device 150, or any othercomponent of the imaging system 100. In some embodiments, the storage220 may include a mass storage device, a removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. For example, the mass storage device mayinclude a magnetic disk, an optical disk, a solid-state drive, etc. Theremovable storage device may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Thevolatile read-and-write memory may include a random access memory (RAM).The RAM may include a dynamic RAM (DRAM), a double date rate synchronousdynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM),and a zero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM(MROM), a programmable ROM (PROM), an erasable programmable ROM (PEROM),an electrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store instructions that the processor 210may execute to perform operations including at least one of: generatingone or more medical images (e.g., breast images), determining a breastregion in a breast image, determining automatic exposure control (AEC)parameters for scanning a breast using the imaging device 110 based onthe determined breast region, determining a collimator region in abreast image, and performing image enhancement to an image.

The I/O 230 may input or output signals, data, or information. In someembodiments, the I/O 230 may enable user interaction with the processingdevice 140. In some embodiments, the I/O 230 may include an input deviceand an output device. Exemplary input devices may include a keyboard, amouse, a touch screen, a microphone, a trackball, or the like, or acombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing device 140 and theimaging device 110, the terminal 130, or the storage device 150. Theconnection may be a wired connection, a wireless connection, or acombination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include Bluetooth, Wi-Fi, WiMax, WLAN, ZigBee, mobilenetwork (e.g., 3G, 4G, 5G, etc.), or the like, or a combination thereof.In some embodiments, the communication port 240 may be a standardizedcommunication port, such as RS232, RS485, etc. In some embodiments, thecommunication port 240 may be a specially designed communication port.For example, the communication port 240 may be designed in accordancewith the digital imaging and communications in medicine (DICOM)protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device on which the terminal 130 may beimplemented according to some embodiments of the present disclosure. Asillustrated in FIG. 3 , the mobile device 300 may include acommunication platform 310, a display 320, a graphics processing unit(GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory360, and a storage 390. In some embodiments, any other suitablecomponent, including but not limited to a system bus or a controller(not shown), may also be included in the mobile device 300. In someembodiments, a mobile operating system 370 (e.g., iOS, Android, WindowsPhone, etc.) and one or more applications 380 may be loaded into thememory 360 from the storage 390 in order to be executed by the CPU 340.The applications 380 may include a browser or any other suitable mobileapps for receiving and rendering information relating to imageprocessing or other information from the processing device 140. Userinteractions with the information stream may be achieved via the I/O 350and provided to the processing device 140 and/or other components of theimaging system 100 via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. The hardware elements, operating systems and programminglanguages of such computers are conventional in nature, and it ispresumed that those skilled in the art are adequately familiar therewithto adapt those technologies to the blood pressure monitoring asdescribed herein. A computer with user interface elements may be used toimplement a personal computer (PC) or another type of work station orterminal device, although a computer may also act as a server ifappropriately programmed. It is believed that those skilled in the artare familiar with the structure, programming and general operation ofsuch computer equipment and as a result the drawings should beself-explanatory.

Breast tissue is mainly composed of fat and glands. Because breasttissue is sensitive to X-rays, before the patient's breast is formallyscanned, it is usually necessary to use a low dose of X-rays to scan thebreast to acquire a pre-exposure breast image. One or more suitableexposure parameters used for a formal scan are obtained based on theanalysis result of the pre-exposure image. During the process foranalyzing the pre-exposure breast image, if the breast region in thebreast image is accurately identified, a more accurate result ofidentifying the gland region may be obtained, and a more suitableexposure parameter for formal scan may be determined based on the glandregion, which ensures that the formal exposure breast image is effectivefor medical diagnosis and reduces the X-ray dose accepted by the patientin the formal scan.

An aspect of the present disclosure may provide systems and/or methodfor determining a breast region in a breast image.

FIG. 4 is a schematic block diagram illustrating an exemplary processingdevice according to some embodiments of the present disclosure. Theprocessing device 140 may include an imaging processing module 410, acurve processing module 420, a location processing module 430, a glanddetermination module 440, and a parameter determination module 450.

The imaging processing module 410 may be configured to obtain a breastimage of an object that is acquired by an imaging device. The curveprocessing module 420 may be configured to determine a projection curvebased on the breast image, determine a first valley point and a secondvalley point of the projection curve and determine a peak point of theprojection curve based the first valley point and the second valleypoint of the projection curve. The location processing module 430 may beconfigured to determine a first valley location, a second valleylocation, and a peak location in the breast image based on the peakpoint, the first valley point, and the second valley point of theprojection curve and determine a breast region in the breast image basedon the first valley location, the second valley location, and the peaklocation. The gland determination module 440 may be configured todetermine a gland region in the determined breast region. The parameterdetermination module 450 may be configured to determine a gray level ofthe gland region, obtain a preset relationship of a pre-exposure X-raydose used to acquire the pre-exposure breast image, a compressionthickness of the breast, the gray level of the gland region, and AECparameters, and determine the AEC parameters based on the presetrelationship, the X-ray dose, the compression thickness of the breast,and the gray level of the gland region.

The modules in the processing device 140 may be connected to orcommunicate with each other via a wired connection or a wirelessconnection. The wired connection may include a metal cable, an opticalcable, a hybrid cable, or the like, or any combination thereof. Thewireless connection may include a Local Area Network (LAN), a Wide AreaNetwork (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC),or the like, or any combination thereof. Two or more of the modules maybe combined as a single module, and any one of the modules may bedivided into two or more units. For example, the imaging processingmodule 410 may be divided into two units One of the two unit may beconfigured to obtain a binary image, and the other one of the two unitmay be configured to obtain a projection curve based on the binaryimage.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, theprocessing device 140 may further include a storage module (not shown inFIG. 4 ). The storage module may be configured to store data generatedduring any process performed by any component of in the processingdevice 140. As another example, each of the components of the processingdevice 140 may include a storage device. Additionally or alternatively,the components of the processing device 140 may share a common storagedevice.

FIG. 5 is a flowchart illustrating an exemplary process for determininga breast region in a breast image according to some embodiments of thepresent disclosure. In some embodiments, the process 500 may beimplemented in the imaging system 100 illustrated in FIG. 1 . Forexample, the process 500 may be stored in a storage medium (e.g., thestorage device 150, or the storage 220 of the processing device 140) asa form of instructions, and can be invoked and/or executed by theprocessing device 140 (e.g., the processor 210 of the processing device140, or one or more modules in the processing device 140 illustrated inFIG. 4 ). The operations of the illustrated process 500 presented beloware intended to be illustrative. In some embodiments, the process 500may be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of the process 500 asillustrated in FIG. 5 and described below is not intended to belimiting.

In 510, the processing device 140 (e.g., the image processing module410) may obtain a breast image of an object that is acquired by animaging device (e.g., the imaging device 110 of the imaging system 100in FIG. 1 ). In some embodiments, the imaging device 110 used here maybe an X-ray device. In some embodiments, the breast image may include abreast of the object (e.g., a patient). In some embodiments, the breastimage may further include other portion (e.g., the thorax, an arm, etc.)of the object. In some embodiments, the breast image may show the topview (e.g., as shown in FIG. 12B) or the lateral view of the breast(e.g., as shown in FIG. 14 ).

In 520, the processing device 140 (e.g., the image processing module410) may determine a projection curve based on the breast image. Merelyby way of example, as shown in FIG. 7 , the breast image 700 may includea body region including a breast of the object (e.g., a patient), adirect exposure region, and a collimator region. The collimator regionin the breast image may correspond to at least a portion of thecollimator in the imaging device 110. The direct exposure region may bea region imaged by the X-rays from the X-ray tube to the detectorwithout penetrating any substance that attenuates the X-rays. Forexample, the direct exposure region may be a region imaged by the X-raysfrom the X-ray tube to the detector without penetrating any human bodysuch as a portion of a breast. As another example, the direct exposureregion may be a region imaged by the X-rays from the X-ray tube to thedetector without penetrating any collimator and human body such as aportion of a breast. As still another example, when the X-rayspenetrates an area which has nothing except air between the X-ray tubeand the detector, the corresponding region imaged by the detector couldbe referred to as a direction exposure region hereinafter. As stillanother example, when the X-rays penetrates an area which has nothingexcept air, the compression pad, and/or the breast-holder tray betweenthe X-ray tube and the detector, the corresponding region imaged by thedetector could be referred to as a direction exposure regionhereinafter.

The processing device 140 may identify the body region and obtain abinary image (e.g., as shown in FIG. 9 ) including the body region. Theprocessing device 140 may obtain the projection curve by determining aplurality of sums each of which is a sum of pixel values of a row ofpixels in the binary image. The row of pixels may be arranged along adirection (e.g., parallel to the X-axis in FIG. 9 ) perpendicular to theextension direction (e.g., parallel to the Y-axis in FIG. 9 ) of achest-wall side of the binary image. The binary image may be a rectangleand have four sides. One of the four sides that is closest to the chestwall in the binary image may be the chest-wall side. As used herein, thechest wall may refer to the boundary of the thorax of the patient. Foreach point on the projection curve, a first coordinate on a firstcoordinate axis (e.g., the vertical coordinate axis of the projectioncurve 1000 in FIG. 10 ) of the point represents a position of a row ofpixels in the binary image, and a second coordinate on a secondcoordinate axis (e.g., the horizontal coordinate axis of the projectioncurve 1000 in FIG. 10 ) of the point represents a sum of pixel values ofthe corresponding row of pixels. Details regarding the determination ofthe projection curve may be found elsewhere in the present disclosure(e.g., the description in connection with FIG. 6 ).

In 530, the processing device 140 (e.g., the curve processing module420) may determine a first valley point and a second valley point of theprojection curve. In some embodiments, the processing device 140 maydetermine the first alley point and the second valley point based on theshape of the projection curve. Details regarding the determination ofthe first valley point and the second valley point may be foundelsewhere in the present disclosure (e.g., the description in connectionwith FIG. 12 ).

In 540, the processing device 140 (e.g., the curve processing module420) may determine a peak point of the projection curve based on thefirst valley point and the second valley point of the projection curve.In some embodiments, the peak point may be located between the firstvalley point and the second valley point. In some embodiments, if thereare more than one candidate peak points between the first valley pointand the second valley point, the processing device 140 may determine thepeak point based on the preset location of the peak point relative tothe first valley point and the second valley point.

In 550, the processing device 140 (e.g., the location processing module430) may determine a first valley location, a second valley location,and a peak location in the breast image based on the peak point, thefirst valley point, and the second valley point of the projection curve.In some embodiments, the first valley location, the second valleylocation, and the peak location in the breast image may correspond tothe first valley point, the second valley point, and the peak valleypoint in the curve projection, respectively.

Taking the first valley location as an example, the processing device140 may determine which pixel row in the binary image the first valleylocation is in based on the first coordinate of the first valley point.The processing device 140 may identify, in that pixel row, the firstpixel of which the pixel value is not 0 along the X-axis direction inFIG. 9 . The processing device 140 may determine the pixel in the breastimage corresponding to the first pixel in the binary image as the firstvalley location.

In 560, the processing device 140 (e.g., the location processing module430) may determine a breast region in the breast image based on thefirst valley location, the second valley location, and the peaklocation. In some embodiments, the first valley location and the secondvalley location may be deemed to represent two points at the junction ofthe breast and the chest wall of the object. The peak location may bedeemed to represent the mammilla of the breast in the breast image. Theprocessing device 140 may determine the breast region by connecting thefirst valley location, the second valley location, and the peaklocation. Details regarding the determination of the breast region maybe found elsewhere in the present disclosure (e.g., the description inconnection with FIG. 18 ).

In some embodiments, the breast region determined in operation 560 maybe used for further processing, for example, processing the determinedbreast region to obtain a breast gland region, a breast lesion region,or other tissue regions of the breast.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 6 is a flowchart illustrating an exemplary process for determininga projection curve according to some embodiments of the presentdisclosure. In some embodiments, the process 600 may be implemented inthe imaging system 100 illustrated in FIG. 1 . For example, the process600 may be stored in a storage medium (e.g., the storage device 150, orthe storage 220 of the processing device 140) as a form of instructions,and can be invoked and/or executed by the processing device 140 (e.g.,the processor 210 of the processing device 140, or one or more modulesin the processing device 140 illustrated in FIG. 4 ). The operations ofthe illustrated process 600 presented below are intended to beillustrative. In some embodiments, the process 600 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 600 as illustrated in FIG. 6 and describedbelow is not intended to be limiting. In some embodiments, operation 520of the process 500 in FIG. 5 may be performed based on the process 600.

In 610, the processing device 140 (e.g., the image processing module410) may divide the breast image into a body region and a backgroundregion. The body region may include a breast of the object. Thebackground region may include a direct exposure region and a collimatorregion (e.g., as shown in FIG. 7 ) in the breast image.

The direct exposure region may be a region imaged by the X-rays from theX-ray tube to the detector without penetrating any substance thatattenuates the X-rays. For example, the direct exposure region may be aregion imaged by the X-rays from the X-ray tube to the detector withoutpenetrating any human body such as a portion of a breast. As anotherexample, the direct exposure region may be a region imaged by the X-raysfrom the X-ray tube to the detector without penetrating any collimatorand human body such as a portion of a breast. As still another example,when the X-rays penetrates an area which has nothing except air betweenthe X-ray tube and the detector, the corresponding region imaged by thedetector could be referred to as a direction exposure regionhereinafter. As still another example, when the X-rays penetrates anarea which has nothing except air, the compression pad, and/or thebreast-holder tray between the X-ray tube and the detector, thecorresponding region imaged by the detector could be referred to as adirection exposure region hereinafter. As a result, the direct exposureregion may be brighter than the body region and the collimator region inthe breast image (e.g., a gray scale image) (e.g., as shown in FIG. 7 ).The processing device 140 may identify the direct exposure region and anundistinguished region based on pixel values (e.g., gray values) ofpixels in the breast image. The undistinguished region may include thebody region and the collimator region undistinguished with each other.The processing device 140 may identify the body region and thecollimator region by performing edge detection to the undistinguishedregion (e.g., as shown in FIG. 8 ). The processing device 140 maycombine the collimator region and the direct exposure region as thebackground region. The above process for determining the body region andthe background region may be easier and provide a more accuratesegmentation result. Details regarding the identification of the bodyregion and the background region may be found elsewhere in the presentdisclosure (e.g., the description in connection with FIG. 11 ).

In 620, the processing device 140 (e.g., the image processing module410) may generate a binary image by designating pixel values of pixelsin the body region as 1 and designating pixel values of pixels in thebackground region as 0 (e.g., as shown in FIG. 9 ). In some embodiments,the processing device 140 may generate the binary image by processingthe body region and the background region using one-hot encoding. Forexample, the processing device 140 may generate the binary image shownin FIG. 9 by performing binarization to the image shown in FIG. 8 usingone-hot encoding.

In 630, the processing device 140 (e.g., the image processing module410) may determine a chest-wall side of the binary image. The binaryimage may be a rectangle and have four sides. One of the four sides thatis closest to the chest wall in the binary image may be the chest-wallside. For example, as shown in FIG. 9 , the right side of the binaryimage 900 may be the chest-wall side.

In 640, the processing device 140 (e.g., the image processing module410) may obtain the projection curve (e.g., as shown in FIG. 10 ) bydetermining a plurality of sums each of which is a sum of pixel valuesof a row of pixels in the binary image. The row of pixels may bearranged along a direction (e.g., parallel to the X-axis in FIG. 9 )perpendicular to the extension direction (e.g., parallel to the Y-axisin FIG. 9 ) of the chest-wall side of the binary image.

As shown in FIG. 10 , for each point on the projection curve, a firstcoordinate on a first coordinate axis (e.g., the horizontal coordinateaxis of the projection curve 1000 in FIG. 10 ) of the point mayrepresent a position of a row of pixels in the binary image, and asecond coordinate on a second coordinate axis (e.g., the verticalcoordinate axis of the projection curve 1000 in FIG. 10 ) of the pointmay represent a sum of pixels values of the corresponding row of pixels.For example, the first coordinate of the point 1010 in the projectioncurve 1000 in FIG. 10 may indicate the 100^(th) row of pixels in thebinary image 900 along the Y-axis direction in FIG. 9 . The secondcoordinate of the point 1010 in the projection curve 1000 in FIG. 10 mayindicate that the sum of pixel values of the 100^(th) row of pixels inthe binary image is 61.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 11 is a flowchart illustrating an exemplary process for determininga body region and a background region according to some embodiments ofthe present disclosure. In some embodiments, the process 1100 may beimplemented in the imaging system 100 illustrated in FIG. 1 . Forexample, the process 1100 may be stored in a storage medium (e.g., thestorage device 150, or the storage 220 of the processing device 140) asa form of instructions, and can be invoked and/or executed by theprocessing device 140 (e.g., the processor 210 of the processing device140, or one or more modules in the processing device 140 illustrated inFIG. 4 ). The operations of the illustrated process 1100 presented beloware intended to be illustrative. In some embodiments, the process 1100may be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of the process 1100 asillustrated in FIG. 11 and described below is not intended to belimiting. In some embodiments, operation 610 of the process 600 in FIG.6 may be performed based on the process 1100.

In 1110, the processing device 140 (e.g., the image processing module410) may determine an image gradient of the breast image by processingthe breast image using a Sobel operator.

In 1120, the processing device 140 (e.g., the image processing module410) may obtain a gradient image (e.g., as shown in FIG. 8 ) byprocessing the image gradient using an adaptive thresholding algorithm.

In 1130, the processing device 140 (e.g., the image processing module410) may identify one or more straight lines (e.g., 810 in FIG. 8 ) inthe gradient image using Hough transform.

In 1140, the processing device 140 (e.g., the image processing module410) may determine the background region and the body region based onthe identified one or more straight lines.

In some embodiments, the process 1100 for determining the body regionand the background region may be easier and provide a more accuratesegmentation result.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 12A is a flowchart illustrating an exemplary process fordetermining a first valley point and a second valley point of aprojection curve according to some embodiments of the presentdisclosure. In some embodiments, the process 1200 may be implemented inthe imaging system 100 illustrated in FIG. 1 . For example, the process1200 may be stored in a storage medium (e.g., the storage device 150, orthe storage 220 of the processing device 140) as a form of instructions,and can be invoked and/or executed by the processing device 140 (e.g.,the processor 210 of the processing device 140, or one or more modulesin the processing device 140 illustrated in FIG. 4 ). The operations ofthe illustrated process 1200 presented below are intended to beillustrative. In some embodiments, the process 1200 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 1200 as illustrated in FIG. 12A anddescribed below is not intended to be limiting. In some embodiments,operation 530 of the process 500 in FIG. 5 may be performed based on theprocess 1200.

In 1210, the processing device 140 (e.g., the curve processing module420) may obtain a preset distance between the first coordinates of thefirst valley point and the second valley point, and preset positions ofthe first valley point and the second valley point in the projectioncurve. The preset distance and the preset positions may be set based onbreast characteristics.

In some embodiment, the preset distance may be a range of values. Thepreset position may be a certain region. In some embodiments, the presetdistance and the preset position may be constant or adjustable dependingon different objects (e.g., patients).

In 1220, the processing device 140 (e.g., the curve processing module420) may determine whether there is any valley point or point whosesecond coordinate is 0.

In 1230, the processing device 140 (e.g., the curve processing module420) may determine the first valley point and the second valley point inthe projection curve based on the determination result, the presetdistance, and the preset positions. The first valley point and thesecond valley point may satisfy the preset distance and the presetpositions. For example, the distance between the first coordinates ofthe first valley point and the second valley point may be within thepreset distance. As another example, the first valley point and thesecond valley point may be within the preset positions.

The first valley point and the second valley point may representlocations at the junction of the breast and the chest wall of the objectin the breast image. However, since the mammilla is a convex structureon the breast, in the projection curve, there may be one or more valleypoints nearby the peak point that representing the mammilla. In theprocess 1200, the preset distance and the preset positions may be usedto prevent such valley points nearby the peak point that representingthe mammilla from being determined as the first valley point and thesecond valley point.

The projection curve may be divided into two sections by a peak of theprojection curve, for example, a first section close to the origin ofthe coordinate system of the projection curve and a second section awayfrom the origin of the coordinate system of the projection curve.

In the first embodiments, if there is no valley point in the projectioncurve and both of the two sides of the projection curve have at leastone point whose second coordinates is 0, the processing device 140 maydetermine two points whose second coordinates are 0 as the first valleypoint and the second valley point. The two points may be in the twosides of the projection curve, respectively. The distance between thefirst coordinates of the two points may satisfy the preset distance. Thefirst valley point and the second valley point may be located in thepreset positions. The first embodiment may correspond to the breastimage showing the top view of the breast (e.g., the breast imageincluding only the breast shown in FIG. 12B and/or the breast imageincluding the breast and tissue, such as 1240, other than the breast ofthe object shown in FIG. 12C).

In the second embodiment, if there is no valley point in the projectioncurve and only one side of the projection curve has at least one pointwhose second coordinates is 0, the processing device 140 may determinethe point whose first coordinate is 1 as the first valley point and apoint whose second coordinate is 0 as the second valley point. Thedistance between the first coordinates of the two points may satisfy thepreset distance. The first valley point and the second valley point maybe located in the preset positions.

In the third embodiment, if there is one valley point in the projectioncurve, the processing device 140 may determine whether the valley pointis an effective valley point that is located in the preset positions. Inresponse to a determination that the valley point is the effectivevalley point, the processing device 140 may determine the valley pointas the first valley point and determine a point whose second coordinateis 0 as the second valley point. The distance between the firstcoordinates of the two points may satisfy the preset distance. The firstvalley point and the second valley point may be located in the presetpositions. In response to a determination that the valley point is notthe effective valley point, the processing device 140 may determine thefirst valley point and the second valley point based on the first orsecond embodiment.

For example, as shown in FIG. 10 , point 1020 (20, 53) and point 1030(145, 0) may be determined as the first valley point and the secondvalley point.

In the fourth embodiments, if there are two or more valley points in theprojection curve, the processing device 140 may determine whether thetwo or more valley points are effective valley points that are locatedin the preset positions. In response to a determination that there aretwo or more effective valley points, the processing device 140 maydetermine two of the two or more effective valley points as the firstvalley point and the second valley point. The distance between the firstcoordinates of the two points may satisfy the preset distance. Inresponse to a determination that there is at most one effective valleypoint, the processing device 140 may determine the first valley pointand the second valley point based on the first, second, or thirdembodiment.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

In some embodiments, in the breast image showing the lateral view of thebreast (e.g., as shown in FIG. 14 and/or FIG. 17 ), the area of tissueother than the breast of the object may be larger than that of thebreast, which may make the determination of the breast regioninaccurate. In this case, the processing device 140 may take measures(e.g., perform the process 1300 in FIG. 13 or the process 1600 in FIG.16 ) to reduce the tissue other than the breast during the process fordetermining the projection curve.

FIG. 13 is a flowchart illustrating an exemplary process for determininga first valley location, a second valley location, and a peak locationaccording to some embodiments of the present disclosure. In someembodiments, the process 1300 may be implemented in the imaging system100 illustrated in FIG. 1 . For example, the process 1300 may be storedin a storage medium (e.g., the storage device 150, or the storage 220 ofthe processing device 140) as a form of instructions, and can be invokedand/or executed by the processing device 140 (e.g., the processor 210 ofthe processing device 140, or one or more modules in the processingdevice 140 illustrated in FIG. 4 ). The operations of the illustratedprocess 1300 presented below are intended to be illustrative. In someembodiments, the process 1300 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 1300 as illustrated in FIG. 13 and described below is notintended to be limiting. In some embodiments, the operation 550 of theprocess 500 in FIG. 5 may be performed based on the process 1300.

In 1310, the processing device 140 (e.g., the curve processing module420) may determine that the second coordinate of the first valley pointor the second valley point is greater than the second coordinate of thepeak point. The determination result may indicate that there is too muchtissue other than the breast of the object in the breast image (e.g., asshown in FIG. 14 ), which may make the determination of the breastregion inaccurate.

In 1320, the processing device 140 (e.g., the curve processing module420) may rotate the binary image so that a straight line connectingpoints in the rotated binary image corresponding to the first valleypoint and the second valley point is parallel to or coincides with theY-axis in FIG. 9 .

In 1330, the processing device 140 (e.g., the curve processing module420) may determine a new projection curve based on the rotated binaryimage. For example, the breast image 1500 shown in FIG. 15 maycorrespond to a rotated binary image. Points 1510-1530 in the breastimage 1500 may correspond to the first valley point, the second valleypoint, and the peak point. When determining the new projection curvebased on the rotated binary image, the processing device 140 maydetermine, without considering pixels in the right side of line 1540connecting the points 1510 and 1520, a plurality of sums each of whichis a sum of pixel values of a row of pixels in the rotated binary image.The row of pixels may be arranged along the X-axis direction in FIG. 15in the rotated binary image. The X-axis and Y-axis in FIG. 15 may besimilar to the X-axis and Y-axis in FIG. 9 . In this way, the newprojection curve of the breast image showing the lateral view of thebreast may be determined without considering or by considering a smallportion of tissue other than the breast in the breast image, which maymake the determination of the breast region more accurate. The newprojection curve may be similar to that of the breast image showing thetop view of the breast (e.g., as shown in FIG. 12B and/or FIG. 12C).

In 1340, the processing device 140 (e.g., the curve processing module420) may update the first valley point, the second valley point, and thepeak point based on the new projection curve. In some embodiments, theprocessing device 140 may update the first valley point, the secondvalley point, and the peak point based on the new projection curve byperforming a process similar to the process 1200 in FIG. 12 .

In 1350, the processing device 140 (e.g., the curve processing module420) may determine the first valley location, the second valleylocation, and the peak location in the breast image based on the updatedfirst valley point, the updated second valley point, and the updatedpeak point.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 16 is a flowchart illustrating an exemplary process for determininga first valley location, a second valley location, and a peak locationaccording to some embodiments of the present disclosure. In someembodiments, the process 1600 may be implemented in the imaging system100 illustrated in FIG. 1 . For example, the process 1600 may be storedin a storage medium (e.g., the storage device 150, or the storage 220 ofthe processing device 140) as a form of instructions, and can be invokedand/or executed by the processing device 140 (e.g., the processor 210 ofthe processing device 140, or one or more modules in the processingdevice 140 illustrated in FIG. 4 ). The operations of the illustratedprocess 1600 presented below are intended to be illustrative. In someembodiments, the process 1600 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 1600 as illustrated in FIG. 16 and described below is notintended to be limiting. In some embodiments, the operation 550 of theprocess 500 in FIG. 5 may be performed based on the process 1600.

In 1610, the processing device 140 (e.g., the curve processing module420) may determine that there is no valley point in the projection curveand there is at least one point whose second coordinate is 0 in only oneside of the peak point of the projection curve. The determination resultmay indicate that there is too much tissue other than the breast of theobject in the breast image (e.g., as shown in FIG. 17 ), which may makethe determination of the breast region inaccurate.

In 1620, the processing device 140 (e.g., the curve processing module420) may rotate the binary image so that a straight line connectingpoints in the rotated binary image corresponding to the first valleypoint and the second valley point is parallel to or coincides with theY-axis in FIG. 9 .

In 1630, the processing device 140 (e.g., the curve processing module420) may determine a new projection curve based on the rotated binaryimage.

In 1640, the processing device 140 (e.g., the curve processing module420) may update the first valley point, the second valley point, and thepeak point based on the new projection curve.

In 1650, the processing device 140 (e.g., the curve processing module420) may determine the first valley location, the second valleylocation, and the peak location in the breast image based on the updatedfirst valley point, the updated second valley point, and the updatedpeak point.

The operations 1620-1650 may be performed similar to the operations1320-1350, respectively.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 18 is a flowchart illustrating an exemplary process for determininga breast region according to some embodiments of the present disclosure.In some embodiments, the process 1800 may be implemented in the imagingsystem 100 illustrated in FIG. 1 . For example, the process 1800 may bestored in a storage medium (e.g., the storage device 150, or the storage220 of the processing device 140) as a form of instructions, and can beinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 of the processing device 140, or one or more modules inthe processing device 140 illustrated in FIG. 4 ). The operations of theillustrated process 1800 presented below are intended to beillustrative. In some embodiments, the process 1800 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 1800 as illustrated in FIG. 18 anddescribed below is not intended to be limiting. In some embodiments,operation 560 of the process 500 in FIG. 5 may be performed based on theprocess 1800.

In 1810, the processing device 140 (e.g., the location processing module430) may determine a first straight line (e.g., line 1960 in FIG. 19 )from the peak location (e.g., point 1930 in FIG. 19 ). The firststraight line may be perpendicular to a second straight line (e.g., line1950 in FIG. 19 ) connecting the first valley location and the secondvalley location (e.g., points 1910 and 1920 in FIG. 19 ).

In 1820, the processing device 140 (e.g., the location processing module430) may determine an intersection (e.g., point 1970 in FIG. 19 ) of thefirst straight line and the chest-wall side (e.g., the right side 1940in FIG. 19 ) of the breast image.

In 1830, the processing device 140 (e.g., the location processing module430) may determine the breast region in the breast image by connectingthe first valley location, the second valley location, and theintersection.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 20 is a flowchart illustrating an exemplary process for determininga gland region according to some embodiments of the present disclosure.In some embodiments, the process 2000 may be implemented in the imagingsystem 100 illustrated in FIG. 1 . For example, the process 2000 may bestored in a storage medium (e.g., the storage device 150, or the storage220 of the processing device 140) as a form of instructions, and can beinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 of the processing device 140, or one or more modules inthe processing device 140 illustrated in FIG. 4 ). The operations of theillustrated process 2000 presented below are intended to beillustrative. In some embodiments, the process 2000 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 2000 as illustrated in FIG. 20 anddescribed below is not intended to be limiting. In some embodiments, theprocess 2000 may be performed after the process 500 in FIG. 5 .

In 2010, the processing device 140 (e.g., the gland determination module440) may determine a gray histogram of the breast region determined inoperation 560 of the process 500 in FIG. 5 . For example, as shown inFIG. 21 , for each point in the gray histogram, a first coordinate on afirst coordinate axis (e.g., the vertical coordinate axis of the grayhistogram in FIG. 21 ) of the point may represent a gray value, and asecond coordinate on a second coordinate axis (e.g., the horizontalcoordinate axis of the gray histogram in FIG. 21 ) of the point mayrepresent the number (or a count) of pixels with the gray value.

In 2020, the processing device 140 (e.g., the gland determination module440) may determine a segmentation threshold by segmenting the grayhistogram. In some embodiments, the processing device 140 may determinethe segmentation threshold using an Otsu algorithm.

In 2030, the processing device 140 (e.g., the gland determination module440) may designate, in the determined breast region, a region in whichgray values of pixels are less than the segmentation threshold as agland region (e.g., region 2210 in FIG. 22 ).

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 23 is a flowchart illustrating an exemplary process for determiningAEC parameters of an imaging device according to some embodiments of thepresent disclosure. In some embodiments, the process 2300 may beimplemented in the imaging system 100 illustrated in FIG. 1 . Forexample, the process 2300 may be stored in a storage medium (e.g., thestorage device 150, or the storage 220 of the processing device 140) asa form of instructions, and can be invoked and/or executed by theprocessing device 140 (e.g., the processor 210 of the processing device140, or one or more modules in the processing device 140 illustrated inFIG. 4 ). The operations of the illustrated process 2300 presented beloware intended to be illustrative. In some embodiments, the process 2300may be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of the process 2300 asillustrated in FIG. 23 and described below is not intended to belimiting.

In 2310, the processing device 140 (e.g., the parameter determinationmodule 450) may obtain a pre-exposure breast image of a breast of anobject that is acquired by an imaging device (e.g., the imaging device110). The imaging device 110 may be an X-ray device. In someembodiments, before acquiring a formal breast image of a breast, theimaging system 100 may acquire a pre-exposure breast image of the breastby scanning the breast using a relatively low dose of X-rays (alsoreferred to as a pre-exposure dose of X-rays). The pre-exposure breastimage may be used to estimate AEC parameters of the imaging device 110used for acquiring the formal breast image of the breast.

Before the breast is scanned by the imaging device 110, the breast maybe compressed to a certain thickness and be fixed to the breast-holdertray by the compression pad of the imaging device 110.

In some embodiments, the processing device 140 may determine thepre-exposure dose of X-rays based on the breast. According to whetherthe glandular tissue content in the breast is greater than 75%, thebreast may be divided into two categories: non-dense breast and densebreast. When the dense breast is scanned to obtain the pre-exposurebreast image, a relatively high dose of X-rays may be used in order toobtain good image quality. When the non-dense breast is scanned toobtain the pre-exposure breast image, a relatively low dose of X-raysmay be used in order to avoid overexposure. Optionally, the pre-exposuredose of X-rays corresponding to the dense breast may be set as: X-raysof 30 kV-35 kV and 20 mA-50 mA (where kV (kilo-volts) and mA(milliampere) represent the dose of X-rays); the pre-exposure dose ofX-rays corresponding to the non-dense breast may be set as: X-rays of 25kv-29 kv and 5 mAs-19 mAs, which can reduce or avoid the under-exposureor over-exposure and improve the image quality.

In 2320, the processing device 140 (e.g., the parameter determinationmodule 450) may determine a breast region in the pre-exposure breastimage. The processing device 140 may determine the breast region basedon the process for determining the breast region disclosed in thepresent disclosure (e.g., the process 500 in FIG. 5 ).

In 2330, the processing device 140 (e.g., the parameter determinationmodule 450) may determine a gland region in the determined breastregion. The processing device 140 may determine the breast region basedon the process 2000 in FIG. 20 .

In 2340, the processing device 140 (e.g., the parameter determinationmodule 450) may determine a gray level of the gland region.

In 2350, the processing device 140 (e.g., the parameter determinationmodule 450) may obtain a preset relationship of the pre-exposure X-raydose, a compression thickness of the breast, the gray level of the glandregion, and AEC parameters.

In 2360, the processing device 140 (e.g., the parameter determinationmodule 450) may determine the AEC parameters based on the presetrelationship, the pre-exposure dose, the compression thickness of thebreast, and the gray level of the gland region.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

With the rapid development of image processing technology, imageenhancement technology has been widely used in biomedical engineering,aerospace and aviation technology, communication engineering and otherfields. In the image enhancement process, the multi-resolution analysisalgorithm is usually used for image decomposition processing. Commonlyused enhancement algorithms include Gauss-Laplace pyramid decomposition,wavelet decomposition, and so on. But the algorithm is to adjust thecoefficients of each decomposition layer by designing some equations andparameters, and finally reconstructed into an enhanced image. Theseadjusted parameters are very numerous, and the decomposition layers thatneed to be adjusted are also very numerous, and the parameter adjustmentis performed manually, and the processing process is complicated andcumbersome.

Another aspect of the present disclosure may provide systems and/ormethod for image enhancement using a machine learning model.

FIG. 24 is a schematic block diagram illustrating an exemplaryprocessing device according to some embodiments of the presentdisclosure. The processing device 140 may include an obtaining module2410, a decomposition module 2420, an enhancement module 2430, and areconstruction module 2440.

The obtaining module 2410 may be configured to obtain an original image.The decomposition module 2420 may be configured to obtain a plurality ofdecomposition coefficients of the original image by decomposing theoriginal image. The enhancement module 2430 may be configured to obtainat least one enhancement coefficient by performing enhancement to atleast one of the plurality of decomposition coefficients using a machinelearning model. The reconstruction module 2440 may be configured toobtain an enhanced image corresponding to the original image based onthe at least one enhancement coefficient.

The modules in the processing device 140 may be connected to orcommunicate with each other via a wired connection or a wirelessconnection. The wired connection may include a metal cable, an opticalcable, a hybrid cable, or the like, or any combination thereof. Thewireless connection may include a Local Area Network (LAN), a Wide AreaNetwork (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC),or the like, or any combination thereof. Two or more of the modules maybe combined as a single module, and any one of the modules may bedivided into two or more units.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, theprocessing device 140 may further include a storage module (not shown inFIG. 24 ). The storage module may be configured to store data generatedduring any process performed by any component of in the processingdevice 140. As another example, each of the components of the processingdevice 140 may include a storage device. Additionally or alternatively,the components of the processing device 140 may share a common storagedevice.

FIG. 25 is a flowchart illustrating an exemplary process for imageenhancement according to some embodiments of the present disclosure. Insome embodiments, the process 2500 may be implemented in the imagingsystem 100 illustrated in FIG. 1 . For example, the process 2500 may bestored in a storage medium (e.g., the storage device 150, or the storage220 of the processing device 140) as a form of instructions, and can beinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 of the processing device 140, or one or more modules inthe processing device 140 illustrated in FIG. 24 ). The operations ofthe illustrated process 2500 presented below are intended to beillustrative. In some embodiments, the process 2500 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 2500 as illustrated in FIG. 25 anddescribed below is not intended to be limiting.

In 2510, the processing device 140 (e.g., the obtaining module 2410) mayobtain an original image. In some embodiments, the original image may bea three-dimensional (3D) image and/or a two-dimensional (2D) image. Insome embodiments, the original image may include an image of at leastone organ or tissue. The organs include, but are not limited to, thebrain, lungs, heart, kidneys, liver, or the like. The tissue mayinclude, but is not limited to, epithelial tissue, connective tissue,neural tissue, muscle tissue, or the like. In some embodiments, theoriginal image may include multiple images of the same type, such as MRimages, CT images, PET-CT images, PET-MR images, or the like. In someembodiments, the original image may include multiple images of differenttypes. Taking the brain MR image as an example, the original image mayinclude multiple images including a T1 weighted image, a T2-weightedimage, a fluid attenuated inversion recovery (FLAIR) sequence image ofthe brain, or the like.

In some embodiments, the processing device 140 may perform apre-processing operation on the original image. The pre-processingoperation may include: adjusting display parameters of the originalimage based on a preset value condition, and obtaining a new image bytransforming the display parameters of the original image. The displayparameter of an image refers to the numerical information that isincluded in the image and may be used to adjust the display effect ofthe image. In some embodiments, the display parameter may include theresolution, size, direction of the image, brightness, contrast,length-width ratio, color, or the like. The value condition may be arange of values preset according to the corresponding display parameter,and the range of values may be set in advance according to the specificapplication situation, for example, the size of the image may be set to512×512. By pre-processing the original image, it is possible to improvethe image quality for image enhancement, speed up the processing speedof the image enhancement, and improve the accuracy of the imageenhancement.

In 2520, the processing device 140 (e.g., the decomposition module 2420)may obtain a plurality of decomposition coefficients of the originalimage by decomposing the original image. In some embodiments, theoriginal image may be decomposed using a multi-resolution analysisalgorithm to obtain a plurality of decomposition coefficients of theoriginal image. The multi-resolution analysis algorithm may include aGauss-Laplace pyramid decomposition algorithm, a wavelet decompositionalgorithm, or the like.

In some embodiments, the Gauss-Laplace pyramid decomposition algorithmmay be used to decompose the original image.

FIG. 28 is a schematic diagram of an exemplary Gauss-pyramiddecomposition algorithm according to some embodiments of the presentdisclosure.

As shown in FIG. 28 , the Gauss-Laplace pyramid decomposition algorithmmay include: performing Gaussian decomposition to the original image,and obtaining a Gaussian pyramid of the multi-layer Gaussian sub-imageG_(j) of the original image and a Laplacian pyramid of the multi-layerLaplacian sub-image L_(j).

In some embodiments, obtaining the Gaussian pyramid may include thefollowing operations. The original image may be used as the 0^(th) layerG₀ of the Gaussian pyramid. The first layer sub-image G₁ may be obtainedby performing low-pass filtering and downsampling to the original image.Then, the second layer sub-image G₂ may be obtained by performinglow-pass filtering and downsampling to the first layer sub-image G₁. Theabove operation may be performed successively until the n^(th) layersub-image G_(n) is obtained, thereby obtaining a Gaussian pyramid havinga multi-layer Gaussian sub-image G_(j) (0<=j<=n). The number ofdecomposition layers n may be the number of layers of the Gaussianpyramid, which may be set in advance. In some embodiments, the number ofdecomposition layers n may be obtained according to the feature of theoriginal image. In some embodiments, the number of decomposition layersn may depend on the location information that needs to be enhanced inthe image. When the original image is a medical image, for an organ ortissue with a relatively high density, the number of decompositionlayers may be 5-7 layers, and for an organ or tissue with a relativelylow density, for example, soft tissue, the number of decompositionlayers may be 7-10 layers. In some embodiments, the low-pass filteringmay be performed to the image using a 5*5 Gaussian convolution kernel.In some embodiments, downsampling the image may include sampling theimage in a step of 2.

In some embodiments, obtaining the Laplacian pyramid may include thefollowing operations. Starting from the top of the Gaussian pyramid,that is, the n^(th) layer, a Gaussian sub-image G_(n-1)′ may be obtainedby performing up-sampling and low-pass filtering to the Gaussiansub-image G_(n) in the n^(th) layer. The Gaussian sub-image G_(n-1)′ mayhave a same resolution as the Gaussian sub-image G_(n-1) originally inthe (n−1)^(th) layer. The difference between G_(n-1) and G_(n-1)′ may bethe Laplacian sub-image L_(n-1) of the (n−1)^(th) layer. A Gaussiansub-image G_(n-2)′ may be obtained by performing up-sampling andlow-pass filtering to the Gaussian sub-image G_(n-1) in the (n−1)^(th)layer. The Gaussian sub-image G_(n-2)′ may have a same resolution as theGaussian sub-image G_(n-2) originally in the (n−2)^(th) layer. Thedifference between G_(n-2) and G_(n-2)′ may be the Laplacian sub-imageL_(n-2) of the (n−2)^(th) layer. The above operation may be performedsuccessively until the Laplacian sub-image L₀ of the 0^(th) layer isobtained, thereby obtaining a Laplacian pyramid having a multi-layerLaplacian sub-image L_(j) (0<=j<=(n−1)). In some embodiments, theup-sampling of the image may include inserting a new element between thepixels using a suitable interpolation algorithm based on the originalimage pixels. The interpolation algorithm may include a conventionalinterpolation, an interpolation based on edge image, a region-basedimage interpolation, or the like. In some embodiments, the up-samplingof the image may include interpolating the image in a step of two.

In some embodiments, the Gaussian sub-image G_(j) (0<=j<=n) and theLaplacian sub-image L_(j) (0<=j<=(n−1)) may be the decompositioncoefficient obtained based on the Gauss-Laplace pyramid decompositionalgorithm.

In some embodiments, the original image may be decomposed using awavelet decomposition algorithm.

FIG. 29 is a schematic diagram of an exemplary wavelet decompositionalgorithm according to some embodiments of the present disclosure.

As shown in FIG. 29 , the wavelet decomposition algorithm may includethe following operations. Firstly, the low frequency component L and thehigh frequency component H of the original image in the horizontaldirection may be obtained by decomposing the original image using thediscrete wavelet transform algorithm. Then, sub-images LL₁, LH₁, HL₁,and HH₁ of the original image may be obtained by performing columndecomposition to the transformed data using the discrete wavelettransform algorithm. Performing a row decomposition and a columndecomposition to an image may be a first-level decomposition to theimage. The sub-image LL₁ may be low-frequency components in thehorizontal and vertical directions. The sub-image LH₁ may be alow-frequency component in the horizontal direction and a high-frequencycomponent in the vertical direction. The sub-image HL₁ may be ahigh-frequency component in the horizontal direction and a low-frequencycomponent in the vertical direction. The sub-image HH₁ may behigh-frequency components in the horizontal and vertical directions. Insome embodiments, a two-level decomposition may be performed to thelow-frequency component LL₁, that is, sub-images LL₂, HL₂, LH₂, and HH₂may be obtained by performing row decomposition and column decompositionto the low-frequency component LL₁ using the discrete wavelet transformalgorithm. By analogy, sub-images LL_((k+1)), HL_((K+1)), LH_((k+1)),and HH_((k+1)) may be obtained by performing (k+1)^(th) decomposition tothe sub-image LL_(k) (1<=k). In some embodiments, the decompositionlevel m may be preset according to specific application conditions, forexample, the decomposition level m may be set to 2. In some embodiments,the wavelet function used in the wavelet transform algorithm may includea Moret wavelet function, a Mexican Hat wavelet function, a Meyerwavelet function, a Haar wavelet function, a db6 wavelet function, asym6 wavelet function, or the like.

In some embodiments, the sub-images LL_(k), HL_(k), LH_(k), and HH_(k)(1<=k) may be the decomposition coefficients obtained based on thewavelet decomposition algorithm.

In 2530, the processing device 140 (e.g., the enhancement module 2430)may obtain at least one enhancement coefficient by performingenhancement to at least one of the plurality of decompositioncoefficients using a machine learning model.

In some embodiments, a coefficient enhancement model may be used toobtain at least one enhancement coefficient by processing thedecomposition coefficients of the original image. In some embodiments, apart of the decomposition coefficients of the original image may beprocessed to obtain corresponding enhancement coefficients. In someembodiments, all of the decomposition coefficients of the original imagemay be processed to obtain corresponding enhancement coefficients. Insome embodiments, the decomposition coefficients may be input into thecoefficient enhancement model one by one to obtain correspondingenhancement coefficients. In some embodiments, all of the decompositioncoefficients may be input together into the coefficient enhancementmodel to obtain the corresponding enhancement coefficients.

In some embodiments, the machine learning model may be a trained deeplearning model. A neural network model may include a deep belief networkmodel, a Visual Geometry Group (VGG) convolutional neural network,OverFeat, Region-Convolutional Neural Network (R-CNN), spatial pyramidpooling network (SPP-Net), Fast R-CNN, Faster R-CNN, Region-based FullyConvolution Network (R-FCN), Deeply Supervised Object Detector (DSOD),or the like.

In some embodiments, the machine learning model may be a coefficientenhancement model. The coefficient enhancement model may be obtainedbased on the following training operations. A training set may beobtained. The training set may include a plurality of sample pairs. Thesample pair may include a sample image and an enhanced imagecorresponding to the sample image. The preliminary model may be trainedusing the training set to obtain a coefficient enhancement model.Details regarding model training may be found elsewhere in the presentdisclosure (e.g., the description in connection with FIG. 4 and/or FIG.5 ).

In some embodiments, the processing device 140 or an external devicecommunicating with the imaging system 100 may provide the trained model.

In some embodiments, when the sample images in the training set aredecomposed using the Gauss-Laplacian pyramid decomposition algorithm,the coefficient enhancement model may perform enhancement to theLaplacian sub-image L_(j) (0<=j<=(n−1)) in the decompositioncoefficients to obtain an enhancement coefficient L_(j)′ (0<=j<=(n−1)).In some embodiments, the Gaussian sub-image G_(j) (0<=j<=n) in thedecomposition coefficients may also be enhanced by the coefficientenhancement model. In some embodiments, both of the Laplacian sub-imageL_(j) (0<=j<=(n−1)) and the Gaussian sub-image G_(j) (0<=j<=n) in thedecomposition coefficients may be enhanced by the coefficientenhancement model.

In some embodiments, when the sample images in the training set aredecomposed using the wavelet decomposition algorithm, the coefficientenhancement model may perform enhancement to one or more of thedecomposition coefficients LL_(k), HL_(k), LH_(k), HH_(k) to obtain oneor more of the corresponding enhancement coefficients LL_(k)′, HL_(k)′,LH_(k)′, HH_(k)′.

In 2540, the processing device 140 (e.g., the reconstruction module2440) may obtain an enhanced image corresponding to the original imagebased on the at least one enhancement coefficient.

In some embodiments, the decomposition coefficients that arereconstructed may have been enhanced, or some of the decompositioncoefficients that are reconstructed may have been enhanced.

In some embodiments, when the Gauss-Laplace pyramid decompositionalgorithm is used to decompose the original image, the imagereconstruction may include performing reconstruction using the enhancedLaplacian sub-images and/or the enhanced Gaussian sub-images to obtainan enhanced image corresponding to the original image. In someembodiments, some or all of the decomposition coefficients obtained bythe Gauss-Laplacian pyramid decomposition algorithm may be replaced withthe enhanced sub-images, and image reconstruction may be performed toobtain an enhanced image corresponding to the original image.

In some embodiments, when the original image is decomposed using thewavelet decomposition algorithm, image reconstruction may includeperforming discrete wavelet inverse transform on each column of imagedata composed of enhancement coefficients LL_(k)′, HL_(k)′, LH_(k)′,HH_(k)′, and performing discrete wavelet inverse transform on each rowof the image data. In this way, the enhanced image corresponding to theoriginal image may be obtained.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 26 is a flowchart illustrating an exemplary process for obtaining atraining set according to some embodiments of the present disclosure.

In some embodiments, the process 2600 may be implemented in the imagingsystem 100 illustrated in FIG. 1 . For example, the process 2600 may bestored in a storage medium (e.g., the storage device 150, or the storage220 of the processing device 140) as a form of instructions, and can beinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 of the processing device 140, or one or more modules inthe processing device 140 illustrated in FIG. 24 ). The operations ofthe illustrated process 2600 presented below are intended to beillustrative. In some embodiments, the process 2600 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 2600 as illustrated in FIG. 26 anddescribed below is not intended to be limiting. In some embodiments, theprocess 2600 may be used to obtain the training set configured totraining a preliminary model to obtain the machine learning model.

As shown in FIG. 26 , the process 2600 for obtaining the training setmay include:

Operation 2610: obtaining a plurality of first decompositioncoefficients of a sample image and a plurality of second decompositioncoefficients of an enhanced image corresponding to the sample image bydecomposing the sample image and the corresponding enhanced image; and.

Operation 2620: determining the plurality of first decompositioncoefficients and the plurality of second decomposition coefficients as asample pair.

In some embodiments, the sample image may be a three-dimensional (3D)image and/or a two-dimensional (2D) image. In some embodiments, thesample image may include an image of at least one organ or tissue. Theorgans include, but are not limited to, the brain, lungs, heart,kidneys, liver, or the like. The tissue may include, but is not limitedto, epithelial tissue, connective tissue, neural tissue, muscle tissue,or the like. In some embodiments, the sample image may include multipleimages of the same type, such as MR images, CT images, PET-CT images,PET-MR images, or the like. In some embodiments, the sample image mayinclude multiple images of different types.

Taking the brain MR image as an example, the sample image may includemultiple images including a T1 weighted image, a T2-weighted image, afluid attenuated inversion recovery (FLAIR) sequence image of the brain,or the like.

In some embodiments, the processing device 140 may perform apre-processing operation on the sample image. The pre-processingoperation may include: adjusting display parameters of the sample imagebased on a preset value condition, and obtaining a new image bytransforming the display parameters of the sample image. The displayparameter of an image refers to the numerical information that isincluded in the image and may be used to adjust the display effect ofthe image. In some embodiments, the display parameter may include theresolution, size, direction of the image, brightness, contrast,length-width ratio, color, or the like. The value condition may be arange of values preset according to the corresponding display parameter,and the range of values may be set in advance according to the specificapplication situation, for example, the size of the image may be set to512×512. By pre-processing the sample image, it is possible to improvethe quality of images used for model training, speed up the trainingspeed, and improve the training accuracy. The distribution condition maybe a preset condition that needs to satisfy based on different displayparameters, for example, an average distribution, a random distribution,and a Gaussian distribution, etc. In some embodiments, according to thepreset distribution condition of the display parameters, the displayparameters of the sample image may be processed to obtain a new sampleimage, thereby obtaining more sample images, which may realize dataamplification, and add training data for training the neural networkmodel.

In some embodiments, the enhanced image may be deemed as an imageobtained after any one of the image processing algorithms performed onthe original image. The image processing algorithms may include, but arenot limited to, denoising, scaling, binarization, grayscale, brightnessadjustment, blurring, equalization, filtering, image segmentation, orthe like. In some embodiments, the enhancement may be further understoodas adding some information or transformed data to the original image bycertain means, selectively highlighting the feature of interest in theimage or suppressing (masking) some unwanted features in the image, sothat the image and the visual response characteristic are matched. Thesample image may be processed by performing a histogram equalizationalgorithm, a gamma conversion algorithm, an exponential imageenhancement algorithm, a logarithmic image enhancement algorithm, or thelike, or any combination thereof to obtain an enhanced imagecorresponding to the sample image. The present disclosure does notimpose any restriction on the type of enhancement processing, and anyimage obtained by the processing of the original image to changeoriginal image's rendering effect may be determined as the enhancedimage.

In some embodiments, the sample image and the enhanced image of thesample image may be decomposed using a multi-resolution analysisalgorithm to obtain a plurality of decomposition coefficients of thesample image and a plurality of decomposition coefficients of theenhanced image. The multi-resolution analysis algorithm may include aGauss-Laplace pyramid decomposition algorithm, a wavelet decompositionalgorithm, or the like.

In some embodiments, the Gauss-Laplace pyramid decomposition algorithmmay be used to decompose the sample image and the enhanced imagecorresponding to the sample image. The Gauss-Lapras Gaussian pyramiddecomposition algorithm has been described in FIG. 28 , and will not bedescribed here.

In some embodiments, Gaussian decomposition may be performed on thesample image G¹ to obtain a Gaussian pyramid of the multi-layer Gaussiansub-image G¹ _(j) of the sample image and a Laplacian pyramid of themulti-layer Laplacian sub-image L¹ _(j). In some embodiments, theenhanced image G¹′ corresponding to the sample image may beGaussian-decomposed to obtain a Gaussian pyramid of the multi-layerGaussian sub-image G¹ _(j)′ of the sample image and a Laplacian pyramidof the multi-layer Laplacian sub-image L¹ _(j)′.

In some embodiments, the number of decomposition layers n may beobtained according to the feature of the sample image, for example, thenumber of decomposition layers n may be set to 3.

In some embodiments, the Gaussian sub-image G¹ _(j) (0<=j<=n) and theLaplacian sub-image L¹ _(j) (0<=j<=(n−1)) may be used as thedecomposition coefficients obtained after the sample image G¹ issubjected to the Gauss-Laplacian pyramid decomposition algorithm.Alternatively, only the Laplacian sub-image L¹ _(j) (0<=j<=(n−1)) may beused as the decomposition coefficient obtained after the sample image G¹is subjected to the Gauss-Laplacian pyramid decomposition algorithm.Both of the Gaussian sub-image G¹ _(j)′ (0<=j<=n) and the Laplaciansub-image L¹ _(j)′ (0<=j<=(n−1)) may be used as decompositioncoefficients obtained by the Gauss-Laplace pyramid decomposition methodof the enhanced image G¹′ corresponding to the sample image.Alternatively, only the Laplacian sub-image L¹ _(j)′ (0<=j<=(n−1)) maybe taken as the decomposition coefficient obtained after theGauss-Laplace pyramid decomposition algorithm is performed on theenhanced image G¹′ corresponding to the sample image.

In some embodiments, the wavelet image decomposition algorithm may beused to decompose the sample image. The wavelet decomposition algorithmhas been described in FIG. 29 , and will not be described here.

In some embodiments, the sample image G² may be subjected to waveletdecomposition processing to obtain sub-images LL² _((k+1)), HL²_((k+1)), LH² _((k+1)), and HH² _((k+1)). In some embodiments, thedecomposition level m may be preset according to specific applicationconditions, for example, the decomposition level m may be set to 2.

In some embodiments, the enhanced image G^(2′) corresponding to thesample image may be subjected to wavelet decomposition processing toobtain sub-images LL^(2′) _((k+1)), HL^(2′) _((k+1)), LH^(2′) _((k+1)),and HH^(2′) _((k+1)). In some embodiments, the decomposition level m maybe preset according to specific application conditions, for example, thedecomposition level m may be set to 2.

In some embodiments, the sub-images LL² _(k), HL² _(k), LH² _(k), andHH² _(k) (1<=k) may be decomposition coefficients obtained after thesample image G² undergoes the wavelet decomposition algorithm. In someembodiments, the sub-images LL^(2′) _((k+1)), HL^(2′) _((k+1)), LH^(2′)_((k+1)), HH^(2′) _((k+1)) may be decomposition coefficients obtainedafter the enhancement image G^(2′) corresponding to the sample image issubjected to the wavelet decomposition algorithm.

In some embodiments, a sample pair may include: a combination of asample image and an enhanced image corresponding to the sample image, ormay include: a plurality of decomposition coefficients of a sample imageand a plurality of decomposition coefficients of the enhanced imagecorresponding to the sample image. For example, one sample pair mayinclude the Gaussian sub-image G¹ of the sample image G¹ (0<=j<=n), theLaplacian sub-image L¹ _(j) (0<=j<=(n−1)), the Gaussian sub-image G¹_(j)′ of the enhanced image corresponding to the sample image G¹(0<=j<=n), the Laplacian sub-image L¹ _(j)′ (0<=j<=(n−1)). As anotherexample, a sample pair may include the Laplacian sub-image L¹ _(j) ofthe sample image G¹ (0<=j<=(n−1)), the Laplacian sub-image L¹ _(j)′ ofthe enhanced image corresponding to the sample image G¹ (0<=j<=(n−1)).

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 27 is a flowchart illustrating an exemplary process for obtaining atraining set according to some embodiments of the present disclosure.

In some embodiments, the process 2700 may be implemented in the imagingsystem 100 illustrated in FIG. 1 . For example, the process 2700 may bestored in a storage medium (e.g., the storage device 150, or the storage220 of the processing device 140) as a form of instructions, and can beinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 of the processing device 140, or one or more modules inthe processing device 140 illustrated in FIG. 24 ). The operations ofthe illustrated process 2700 presented below are intended to beillustrative. In some embodiments, the process 2700 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 2700 as illustrated in FIG. 27 anddescribed below is not intended to be limiting. In some embodiments, theprocess 2700 may be used to obtain the training set configured totraining a preliminary model to obtain the machine learning model.

As shown in FIG. 27 , the process 2700 for obtaining the training setmay include:

Operation 2710: obtaining a sample image;

Operation 2720: obtaining a plurality of decomposition coefficients ofthe sample image by decomposing the sample image;

Operation 2730: obtaining a plurality of enhanced coefficients byperforming enhancement to the decomposition coefficients of the sampleimage; and.

Operation 2740: determining the plurality of decomposition coefficientsof the sample image and the plurality of corresponding enhancedcoefficients as a sample pair.

Details regarding the sample image may be found elsewhere in the presentdisclosure (e.g., the description in connection with FIG. 26 ).

In some embodiments, a pre-processing operation may be performed on thesample image. Details regarding the pre-processing may be foundelsewhere in the present disclosure (e.g., the description in connectionwith FIG. 26 ).

In some embodiments, the sample image may be decomposed using amulti-resolution analysis algorithm to obtain a plurality ofdecomposition coefficients of the sample image. The multi-resolutionanalysis algorithm may include a Gauss-Laplace pyramid decompositionalgorithm, a wavelet decomposition algorithm, or the like.

In some embodiments, the Gauss-Laplace pyramid decomposition algorithmmay be used to decompose the sample image. The Gauss-Lapras Gaussianpyramid decomposition algorithm has been described in detail in FIG. 28, and will not be described here.

In some embodiments, Gaussian decomposition may be performed on thesample image G³ to obtain a Gaussian pyramid of the multi-layer Gaussiansub-image G³ _(j) of the sample image G³ and a Laplacian pyramid of themulti-layer Laplacian sub-image L³ _(j).

In some embodiments, the number of decomposition layers n may beobtained according to the feature of the sample image, for example, thenumber of decomposition layers n may be set to 3.

In some embodiments, the Gaussian sub-image G³ _(j) (0<=j<=n) and theLaplacian sub-image L³ _(j) (0<=j<=(n−1)) may be the decompositioncoefficients obtained after the sample image G³ is decomposed by theGauss-Laplacian pyramid algorithm.

In some embodiments, only the Laplacian sub-image L³ _(j) (0<=j<=(n−1))may be taken as the decomposition coefficient obtained after the sampleimage G³ is subjected to the Gauss-Laplacian pyramid decompositionalgorithm.

In some embodiments, the wavelet image decomposition algorithm may beused to decompose the sample image. The wavelet decomposition algorithmhas been described in detail in FIG. 29 , and will not be describedhere.

In some embodiments, the sample image G⁴ may be subjected to waveletdecomposition processing to obtain sub-images LL⁴ _((k+1)), HL⁴_((k+1)), LH⁴ _((k+1)), and HH⁴ _((k+1)). In some embodiments, thedecomposition level m may be preset according to specific applicationconditions, for example, the decomposition level m may be set to 2.

In some embodiments, the sub-images LL⁴ _(k), HL⁴ _(k), LH⁴ _(k), andHH⁴ _(k) (1<=k) may be decomposition coefficients obtained after thesample image G⁴ is subjected to the wavelet decomposition algorithm.

In some embodiments, the enhanced image may be deemed as an imageobtained after any one of the image processing algorithms performed onthe original image. The image processing algorithms may include, but arenot limited to, denoising, scaling, binarization, grayscale, brightnessadjustment, blurring, equalization, filtering, image segmentation, orthe like. In some embodiments, the enhancement may be further understoodas adding some information or transformed data to the original image bycertain means, selectively highlighting the feature of interest in theimage or suppressing (masking) some unwanted features in the image, sothat the image and the visual response characteristic are matched. Thesample image may be processed by performing a histogram equalizationalgorithm, a gamma conversion algorithm, an exponential imageenhancement algorithm, a logarithmic image enhancement algorithm, or thelike, or any combination thereof to obtain an enhanced imagecorresponding to the sample image. In some embodiments, a singlethreshold enhancement algorithm, a dual threshold enhancement algorithm,an adaptive enhancement algorithm, or the like, or any combinationthereof may be performed on the decomposition coefficients to obtainenhancement coefficients. For example, the gray value of thedecomposition coefficient may be normalized to obtain a normalizeddecomposition coefficient, and the normalized decomposition coefficientmay be subjected to a power function transformation. The contrastequalization process may be performed to obtain the enhancementcoefficient after the equalization process, which may be set accordingto the specific application situation. As another example, the powerfunction may be a square function. In some embodiments, the waveletdecomposition may be performed on the decomposition coefficient toobtain the enhancement coefficient. The wavelet denoising processing mayinclude wavelet transform modulus maximum value denoising algorithm,wavelet coefficient correlation denoising algorithm, wavelet transformthreshold denoising algorithm, or the like. In some embodiments, theenhancement may be performed on one or more of the decompositioncoefficients corresponding to the sample image to obtain one or more ofthe enhancement coefficients. For more enhancements, see the prior art:(1) Research on image enhancement processing algorithm based on wavelettransform, Xiang Cong, Tao Yongpeng, Computer and Digital Engineering,No. 8, 2017; (2) Digital Medicine Image Enhancement Based on PyramidMethod, Chen Xiaolong, Chen Gang, Wang Yi, No. 5, 2015; (3) Mammographybased on binary wavelet and PDE image enhancement, Tang Quan, HuangYunqi, Electronic Design Engineering, No. 5, 2018. The presentdisclosure does not impose any restriction on the type of enhancementprocessing, and any image obtained by the processing of the originalimage to change original image's rendering effect may be determined asthe enhanced image.

In some embodiments, the training set may include decompositioncoefficients and corresponding enhancement coefficients, or sampleimages and corresponding enhanced images.

In some embodiments, when the Gaussian sub-image G³ _(j) (0<=j<=n) andthe Laplacian sub-image L³ _(j) (0<=j<=(n−1)) may be the decompositioncoefficients of the sample image G³. The Laplacian sub-image L³ _(j) maybe subjected to enhancement processing to obtain enhancementcoefficients L³ _(j)′ (0<=j<=(n−1)) and G³ _(j)′ (0<=j<=n). The trainingset may include the Gaussian sub-image G³ _(j) (0<=j<=n), the Laplaciansub-image L³ _(j) (0<=j<=(n−1)), the enhancement coefficient L³ _(j)′(0<=j<=(n−1)) and G³ _(j)′ (0<=j<=n).

In some embodiments, when the Laplacian sub-image L³ _(j) (0<=j<=(n−1))may be the decomposition coefficient of the sample image G³. TheLaplacian sub-image L³ _(j) may be subjected to enhancement processingto obtain an enhancement coefficient L³ _(j)′ (0<=j<=(n−1)). Thetraining set may include the Laplacian sub-image L³ _(j) (0<=j<=(n−1))and the enhancement coefficient L³ _(j)′ (0<=j<=(n−1)).

In some embodiments, when the sub-images LL⁴ _(k), HL⁴ _(k), LH⁴ _(k),and HH⁴ _(k) (1<=k) may be the decomposition coefficients of the sampleimage G⁴. The enhancement processing may be performed on one or more ofthe sub-pictures LL⁴ _(k), HL⁴ _(k), LH⁴ _(k), and HH⁴ _(k) to obtaincorresponding enhancement coefficients LL⁴ _(k)′, HL⁴ _(k)′, LH⁴ _(k)′,and HH⁴ _(k)′. The training set may include enhancement coefficients LL⁴_(k)′, HL⁴ _(k)′, LH⁴ _(k)′, and HH⁴ _(k)′ corresponding to thesub-images LL⁴ _(k), HL⁴ _(k), LH⁴ _(k), and HH⁴ _(k) (1<=k).

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

A coefficient enhancement model may be obtained by performing machinelearning on the sample images and the enhanced images processed by imageenhancement. It is possible to perform an independent adaptiveenhancement process for each image through the coefficient enhancementmodel, which reduces the difficulty of adjusting the enhancement effectand improves the image quality. It should be noted that differentembodiments may have different beneficial effects. In differentembodiments, the beneficial effects that may be produced may be anycombination of one or more of the above, and may be any other beneficialeffects that may be obtained.

During the image acquiring operation on the compressed breast tissue,there may be a collimator region in the acquired breast image, and sincethe collimator is a substance with high X-ray attenuation, there will bea high attenuation region in the corresponding location of the breastimage. The high attenuation region increases the complexity anddifficulty for processing the breast image, thereby reducing theprocessing effect of the breast image. Conventionally, the highattenuation region corresponding to the collimator in the breast imageis removed by cropping the acquired breast image according to themechanical feedback coordinates of the collimator. However, due todefects such as mechanical position errors, the collimator regionremains in the cropped breast image.

In order to solve the traditional deficiencies based on mechanicalfeedback coordinates, yet another aspect of the present disclosure mayprovide systems and/or methods for determining a collimator region in abreast image.

FIG. 30 is a schematic block diagram illustrating an exemplaryprocessing device according to some embodiments of the presentdisclosure. The processing device 140 may include an obtaining module3010, a binary template obtaining module 3020, a gradient imageobtaining module 3030, a region determination module 3040, a featureidentification module 3050, and an edge determination module 3060.

The obtaining module 3010 may be configured to obtain a breast image ofan object that is acquired by an imaging device. The binary templateobtaining module 3020 may be configured to obtain a binary templateincluding a direct exposure region of the breast image. The gradientimage obtaining module 3030 may be configured to obtain a binarygradient image by performing gradient transform and binarization to thebreast image, the binary gradient image including one or more straightline features. The region determination module 3040 may be configured todetermine a preliminary region based on the binary template and thebinary gradient image. The edge determination module 3060 may beconfigured to process at least one of the breast image, the binarytemplate, and the binary gradient image to reduce an effect ofoverexposure or tissue of the object with high X-ray attenuation in thebreast image on the one or more straight line features. The featureidentification module 3050 may be configured to identify the one or morestraight line features in the binary gradient image based on theprocessing result. The edge determination module 3060 may be furtherconfigured to determine an edge of a collimator of the imaging device inthe preliminary region based on the identified one or more straight linefeatures, the edge including at least one of the identified one or morestraight line features each of which has a length longer than a lengththreshold and is out of the direct exposure region.

The modules in the processing device 140 may be connected to orcommunicate with each other via a wired connection or a wirelessconnection. The wired connection may include a metal cable, an opticalcable, a hybrid cable, or the like, or any combination thereof. Thewireless connection may include a Local Area Network (LAN), a Wide AreaNetwork (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC),or the like, or any combination thereof. Two or more of the modules maybe combined as a single module, and any one of the modules may bedivided into two or more units.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, theprocessing device 140 may further include a storage module (not shown inFIG. 30 ). The storage module may be configured to store data generatedduring any process performed by any component of in the processingdevice 140. As another example, each of the components of the processingdevice 140 may include a storage device. Additionally or alternatively,the components of the processing device 140 may share a common storagedevice.

FIG. 31A is a flowchart illustrating an exemplary process fordetermining an edge of a collimator of an imaging device in a breastimage according to some embodiments of the present disclosure.

In some embodiments, the process 3100 may be implemented in the imagingsystem 100 illustrated in FIG. 1 . For example, the process 3100 may bestored in a storage medium (e.g., the storage device 150, or the storage220 of the processing device 140) as a form of instructions, and can beinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 of the processing device 140, or one or more modules inthe processing device 140 illustrated in FIG. 30 ). The operations ofthe illustrated process 3100 presented below are intended to beillustrative. In some embodiments, the process 3100 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 3100 as illustrated in FIG. 31A anddescribed below is not intended to be limiting.

In 3110, the processing device 140 (e.g., the obtaining module 3010) mayobtain a breast image of an object that is acquired by an imaging device(e.g., the imaging device 110). In some embodiments, the imaging device110 used here may be an X-ray device.

For example, the breast may be compressed by the compression pad of theimaging device 110. The compression pad may be a large compression pad,a small compression pad, a point compression pad, an axilla compressionpad, or the like. The opening of the collimator of the imaging system100 may be adjusted to match the type of the compression pad. Then thecompressed breast may be scanned by the imaging device 110 to obtain thebreast image.

In some embodiments, the processing device 140 may pre-process thebreast image. For example, the processing device 140 may perform atleast one of normalization, gray linearization, bilinear interpolation,and median filtering to the breast image. The subsequent operations(e.g., operations 3120-3160) may be performed based on the pre-processedbreast image.

Taking normalization as an example, the breast image may be normalizedsuch that the pixel values in the normalized breast image (e.g., theimage in FIG. 33A) may be within a range of 0-1.

The normalization process may include: first obtaining a maximum pixelvalue P_(max) and a minimum pixel value P_(min) in the breast image;determining a first result by subtracting the minimum pixel valueP_(min) from the maximum pixel value P_(max); determining a secondresult by subtracting the minimum pixel value P_(min) from the pixelvalue P_(n) (n is a positive integer) of a pixel; and determining athird result by dividing the second result by the first result. Thethird result may be the normalized pixel value P_(n)′ of the pixel,P_(n)′=(P_(n)−P_(min))/(P_(max)−P_(min)). In this way, each pixel valuein the normalized breast image may be within the range of 0-1.

In some embodiments, the processing device 140 may perform graylinearization and median filtering to the breast image to improve theaccuracy of the collimator region detection. For example, as shown inFIG. 31B, the breast image 11 may include a bad line 12 that may affectthe detection of the straight line features. The processing device 140may perform gray linearization and median filtering to the breast image11 to remove the bad line 12 from the breast image 11, thereby obtainingthe pre-processed breast image 13 in FIG. 31C without the bad line.

For example, the processing device 140 may subtract the global offset ofthe detector of the imaging device 110 from the gray values of thebreast image 11 and set the results that are less than 0 as 0, therebyrealizing the gray linearization performed on the breast image 11.

In some embodiments, the processing device 140 may shrink the breastimage by a first shrinking scale (e.g., 10%) by performing bilinearinterpolation to the breast image. Since the subsequent operations(e.g., operations 3120-3160) may be performed based on the shrunk breastimage that is smaller than the original breast image, the requiredprocessing resource may be reduced and the processing speed may beimproved. The edge of the collimator determined based on the shrunkbreast image may be magnified by the first shrinking scale to obtain theactual edge of the collimator.

In some embodiments, if the processing device 140 shrinks the breastimage by the first shrinking scale, the processing device 140 may alsoshrink the binary template by the first shrinking scale using, forexample, nearest neighbor interpolation.

In 3120, the processing device 140 (e.g., the binary template obtainingmodule 3020) may obtain a binary template including a direct exposureregion of the breast image.

In some embodiments, the direct exposure region may indicate that fromthe X-ray tube to a part of the detector corresponding to the directexposure region, the X-rays do not go through the substance thatattenuates the X-rays. For example, from the X-ray tube to the part ofthe detector corresponding to the direct exposure region, the X-rays maygo through the compression pad and the breast-holder tray, without goingthrough the object and the collimator. As a result, the direct exposureregion may be brighter than one or more other regions in the breastimage (e.g., a gray scale image). The processing device 140 may identifythe direct exposure region based on pixel values (e.g., gray values) ofpixels of the breast image. For example, the processing device 140 maydetermine that pixels with pixel values larger than a pixel thresholdbelong to the direct exposure region. The processing device 140 mayobtain the binary template by designating the pixel values of pixels inthe direct exposure region as 1 and designating the pixel values ofpixels in the other region as 0.

If there is overexposure in the breast image, the overexposure may causethe direct exposure region to be too large, and even to cover thecollimator region (e.g., the region 31 in FIG. 36A), which may cause thestraight line features corresponding to the edge of the collimatordetected based on line detection to be discarded.

In this case, the processing device 140 may perform erosion to thedirect exposure region in the binary template using a first erosionkernel or a second erosion kernel. A size of the second erosion kernelmay be larger than that of the first erosion kernel.

The processing device 140 may obtain a high-gray template (e.g., theimage in FIG. 36C) based on the breast image and a first gray threshold(e.g., 40000-60000). The high-gray template may include a first grayregion (e.g., the region 33 in FIG. 36C) in which gray values of pixelsare greater than or equal to the first gray threshold. The processingdevice 140 may determine whether a ratio of a size of the first grayregion in the high-gray template to a size of the direct exposure regionin the binary template (e.g., the region 32 in FIG. 36B) is greater thana ratio threshold. The processing device 140 may perform the erosion tothe direct exposure region in the binary template based on adetermination result.

In some embodiments, the determination result may include that the ratioof the size of the first gray region to the size of the direct exposureregion is greater than the ratio threshold (e.g., 90%), which indicatesthat there is overexposure in the breast image. The processing device140 may perform the erosion to the direct exposure region in the binarytemplate using the second erosion kernel.

In some embodiments, the determination result may include that the ratioof the size of the first gray region to the size of the direct exposureregion is less than the ratio threshold, which indicates that there isno overexposure in the breast image. The processing device 140 mayperform the erosion to the direct exposure region in the binary templateusing the first erosion kernel or perform no erosion to the directexposure region.

Among them, the “kernel” in the first erosion kernel and the seconderosion kernel may be of any shape and size, and the kernel may have ananchor point. For example, the kernel K1 in the second erosion kernelmay be a matrix of 5*5 and the kernel K2 in the first erosion kernel maybe a matrix of 3*3.

In 3130, the processing device 140 (e.g., the gradient image obtainingmodule 3030) may obtain a binary gradient image by performing gradienttransform and binarization to the breast image. The binary gradientimage may include one or more straight line features.

In some embodiments, operation 3130 may be performed before, after, orsimultaneously with operation 3120.

In some embodiments, since the collimator may not be applied on the sideclose to the patient's body during a breast scan, there may be nocollimator on the side of the breast image close to the chest wall. Thebreast image may be a rectangle and have four sides. One of the foursides that is close to the chest wall in the breast image may be thechest-wall side. As a result, the breast image may include a chest-wallside (e.g., the right side of images shown in the figures of the presentdisclosure), a side opposite to the chest-wall side (e.g., the left sideof images shown in the figures of the present disclosure), an upperside, and a lower side. It is necessary to identify the straight linefeatures related to the upper side, the lower side, and the left side ofthe breast image.

In some embodiments, the binary gradient image may include a firstsub-image, a second sub-image, a third sub-image, and a fourthsub-image.

In some embodiments, the processing device 140 may process the breastimage by performing gradient transform and binarization to the breastimage. The processing device 140 may obtain the first sub-image based ona first gradient threshold and the processing result. The firstsub-image may represent a contour feature of the breast image. Theprocessing device 140 may obtain the second sub-image associated withthe upper side, the third sub-image associated with the lower side, andthe third sub-image associated with the left side based on a secondgradient threshold and the processing result. The first gradientthreshold may be greater than the second gradient threshold.

FIG. 32A is a schematic diagram showing an exemplary edge of acollimator according to some embodiments of the present disclosure. FIG.32B is a schematic diagram of an exemplary gray value feature curverelated to the edge of the collimator in FIG. 32A according to someembodiments of the present disclosure. Here, for each point on the curvein FIG. 32B, a first coordinate on a first coordinate axis (e.g., thevertical coordinate axis in FIG. 32B) of the point represents a grayvalue of a pixel in the image in FIG. 32A, and a second coordinate on asecond coordinate axis (e.g., the horizontal coordinate axis in FIG.32B) of the point represents the distance from the pixel to the leftside of the image in FIG. 32A. Referring to FIGS. 32A and 32B, in anystraight line (e.g., line 3210) parallel to the upper side of the imagein FIG. 32A, a relationship of the distance D between a pixel and theleft side and the gray value G of the pixel may be shown in FIG. 32B.Referring to the shape of the curve L in FIG. 32B, the gray values G ofpixels nearby the edge (e.g., edge 3220) of the collimator change in asloping trend, of which the first derivative can be regarded as aconstant. Therefore, a stepwise operator, for example, a Sobel operatoror a Hough transform, may be used to obtain straight line features anddetect the edge of the collimator.

In some embodiments, the Sobel operator may include a horizontaloperator and a vertical operator. The processing device 140 may extractone or more straight line features in the horizontal and verticaldirections based on the operator matrix, the horizontal directionEquation (1), the vertical direction Equation (2), and Equation (3)related to the whole gradient of the breast image below:

$\begin{matrix}{{g_{x} = {\frac{\partial f}{\partial x} = {( {z_{7} + {2z_{8}} + z_{9}} ) - ( {z_{1} + {2z_{2}} + z_{3}} )}}},} & (1)\end{matrix}$ $\begin{matrix}{{g_{y} = {\frac{\partial f}{\partial x} = {( {z_{3} + {2z_{6}} + z_{9}} ) - ( {z_{1} + {2z_{4}} + z_{7}} )}}},} & (2)\end{matrix}$ and $\begin{matrix}{{M( {x,y} )} \approx {{❘g_{x}❘} + {{❘g_{y}❘}.}}} & (3)\end{matrix}$ $\underset{{Operator}{matrix}{diagram}}{\begin{matrix}z_{1} & z_{2} & z_{3} \\z_{4} & z_{5} & z_{6} \\z_{7} & z_{8} & z_{9}\end{matrix}}\underset{\begin{matrix}{{Operator}{matrix}{in}{the}} \\{{horizontal}{direction}}\end{matrix}}{\begin{matrix}{- 1} & {- 2} & {- 1} \\0 & 0 & 0 \\1 & 2 & 1\end{matrix}}\underset{\begin{matrix}{{Operator}{matrix}{in}{the}} \\{{vertical}{direction}}\end{matrix}}{\begin{matrix}{- 1} & 0 & 1 \\{- 2} & 0 & 2 \\{- 1} & 0 & 1\end{matrix}}$

In the horizontal straight line features extracted by Equation (1), thegradient value related to the upper edge of the collimator region may bea positive value, and the gradient value related to the lower edge ofthe collimator region may be a negative value. In the vertical straightline features extracted by Equation (2), the gradient value related tothe left edge of the collimator region may be a positive value. The edgeof the collimator region may be determined more accurately by extractthe straight line features using the Sobel operator, which may reducethe complexity and difficulty of image processing, and improve theaccuracy of the detection and positioning of the collimator region.

For example, the processing device 140 may obtain a binary gradientimage in FIG. 33B (the first sub-image) by processing the breast image3310 in FIG. 33A by performing gradient transform using Equation (3)based on a first gradient threshold (T1) and binarization to the breastimage. The first sub-image may include the edge 3320 (e.g., the wholeSobel straight line features) of the collimator region. The processingdevice 140 may obtain a binary gradient image in FIG. 33C (the secondsub-image) by processing the breast image in FIG. 33A by performinggradient transform using Equation (1) based on a second gradientthreshold (T2) and binarization to the breast image. The secondsub-image may include the straight line features related to the upperedge 3330 of the collimator region. The processing device 140 may obtaina binary gradient image in FIG. 33D (the third sub-image) by processingthe breast image in FIG. 33A by performing gradient transform usingEquation (1) based on the second gradient threshold (T2) andbinarization to the breast image. The third sub-image may include thestraight line features related to the lower edge 3350 of the collimatorregion. The processing device 140 may obtain a binary gradient image inFIG. 33E (the fourth sub-image) by processing the breast image in FIG.33A by performing gradient transform using Equation (2) based on thesecond gradient threshold (T2) and binarization to the breast image. Thefourth sub-image may include the straight line features related to theleft edge 3360 of the collimator region. The first gradient thresholdmay be larger than the second gradient threshold (T1>T2). In someembodiments, there may be interfering features (e.g., 3340 and/or whitepoints in FIG. 33C) in the binary gradient image.

In 3140, the processing device 140 (e.g., the region determinationmodule 3040) may determine a preliminary region based on the binarytemplate and the binary gradient image. In some embodiments, thepreliminary region may include and be larger than the direct exposureregion.

In some embodiments, the processing device 140 may determine thepreliminary region in the breast image based on the binary template andthe first sub-image.

In some embodiments, the processing device 140 may determine a fourthregion corresponding to the direct exposure region in the binarytemplate based on a first edge feature of the binary template. Theprocessing device 140 may determine a fifth region corresponding to thedirect exposure region in the binary gradient image (e.g., the firstsub-image) based on a second edge feature of the binary gradient image.The processing device 140 may determine a union set of the fourth regionand the fifth region as the preliminary region.

For example, the image 61 in FIG. 34A is a pre-processed breast image.The image in FIG. 34B may be the first sub-image obtained based on theimage 61. The image in FIG. 34B may include the region 62 correspondingto the direct exposure region. The image in FIG. 34C may be the binarytemplate obtained based on the image 61. The image in FIG. 34C mayinclude the region 63 corresponding to the direct exposure region. Theprocessing device 140 may determine the union set of the region 62 andthe region 63 as the preliminary region 64 shown in FIG. 33D.

In some embodiments, the processing device 140 may obtain a physicalposition of the collimator in the imaging device 110. The processingdevice 140 may project the collimator on the original breast image basedon the physical position of the collimator. The processing device 140may obtain the cropped breast image (e.g., the image 41 shown in FIG.35A) by cropping the original breast image along the projection of thecollimator.

The image in FIG. 35B may be the first sub-image obtained based on theimage 41. The image in FIG. 35B may include the region 42 correspondingto the direct exposure region. The image in FIG. 35C may be the binarytemplate obtained based on the image 41. The image in FIG. 35C mayinclude the region 43 corresponding to the direct exposure region. Theprocessing device 140 may determine the union set of the region 42 andthe region 43 as the preliminary region 44 shown in FIG. 35D.

In 3150, the processing device 140 (e.g., the feature identificationmodule 3050) may identify the one or more straight line features in thebinary gradient image. In some embodiments, the processing device 140may identify the one or more straight line features in the binarygradient image using Hough transform. In some embodiments, theprocessing device 140 may identify the one or more straight linefeatures in the binary gradient image by determining one or more rowprojection values and one or more column projection values. Each of theone or more row projection values may be a sum of pixel values of a rowof pixels in the binary gradient image. Each of the one or more columnprojection values may be a sum of pixel values of a column of pixels inthe binary gradient image. A row of pixels may be arranged along adirection perpendicular to the extension direction of the chest-wallside. A column of pixels may be arranged along the extension directionof the chest-wall side.

In 3160, the processing device 140 (e.g., the edge determination module3060) may determine an edge of a collimator of the imaging device in thepreliminary region based on the identified one or more straight linefeatures. The edge may include at least one of the identified one ormore straight line features each of which has a length longer than alength threshold and is out of the direct exposure region.

In some embodiments, the determined edge of the collimator in the breastimage may include the identified straight line features including a rowof pixels associated with the upper side, a row of pixels associatedwith the lower side, and a column of pixels associated with the leftside. The row projection value of the row of pixels associated with theupper side may be a first projection value, the row projection value ofthe row of pixels associated with the lower side may be a secondprojection value, and the column projection value of the column ofpixels associated with the side opposite to the chest-wall side may be athird projection value. The length threshold may include a first lengththreshold, a second length threshold, and a third length threshold. Thefirst projection value may be greater than the first length threshold,the second projection value may be greater than the second lengththreshold, and the third projection value may be greater than the thirdlength threshold.

In some embodiments, a ratio of the first length threshold to a lengthof an upper side or a lower side of the preliminary region may be equalto a first preset value (e.g., 90%, 80%, 70%, 60%, etc.), a ratio of thethird length threshold to a length of an edge opposite to the chest-wallside of the preliminary region may be equal to a second preset value(e.g., 90%, 80%, 70%, 60%, etc.), and the first length threshold may beequal to the second length threshold. Further, the first preset valueand/or the second preset value may be larger than 80%.

In some embodiments, the processing device 140 may determine the firstprojection value in the second sub-image. The first projection value maybe a maximum row projection value in the second sub-image. Theprocessing device 140 may determine the second projection value in thethird sub-image. The second projection value may be a maximum rowprojection value in the third sub-image. The processing device 140 maydetermine the third projection value in the fourth sub-image. The thirdprojection value may be a maximum column projection value in the fourthsub-image.

In some embodiments, the processing device 140 may determine a centerpoint of the preliminary region. For the straight line feature with thefirst projection value related to the upper side, the processing device140 may determine whether there is at least a portion of the directexposure region in the side of the straight line feature away from thecenter point. In response to a determination that there is no directexposure region in the side of the straight line feature away from thecenter point, the processing device 140 may determine the straight linefeature as the upper edge of the collimator in the breast image. For thestraight line feature with the second projection value related to thelower side, the processing device 140 may determine whether there is atleast a portion of the direct exposure region in the side of thestraight line feature away from the center point. In response to adetermination that there is no direct exposure region in the side of thestraight line feature away from the center point, the processing device140 may determine the straight line feature as the lower edge of thecollimator in the breast image. For the straight line feature with thethird projection value related to the left side, the processing device140 may determine whether there is at least a portion of the directexposure region in the side of the straight line feature away from thecenter point. In response to a determination that there is no directexposure region in the side of the straight line feature away from thecenter point, the processing device 140 may determine the straight linefeature as the left edge of the collimator in the breast image.

In some embodiments, the processing device 140 may add a make-up part toat least one of the one or more straight line features in thepreliminary region. The make-up part may be in a region (e.g., a lowgray region) corresponding to tissue of the object with high X-rayattenuation.

The processing device 140 may obtain a first low-gray template (e.g.,the image 73 in FIG. 37C) based on the breast image (e.g., the image 71in FIG. 37A) and a second gray threshold (e.g., 50-100). The firstlow-gray template may include a first region representing the tissue ofthe object with high X-ray attenuation. The processing device 140 maydetermine a second region by performing dilation to the first regionusing a dilation kernel to generate a second low-gray template (e.g.,the image 74 in FIG. 37D). The second region may be larger than thefirst region. The processing device 140 may obtain a third low-graytemplate (e.g., the image 75 in FIG. 37E) by removing a region otherthan the preliminary region from the second low-gray template. The thirdlow-gray template may include a third region (e.g., the region 77 inFIG. 37E) corresponding to the second region. The third region may besmaller than the second region. The processing device 140 may add themake-up part (e.g., the line 78 in FIG. 37F) to the at least one of theone or more straight line features (e.g., the straight line feature inthe third sub-image 72 in FIG. 37B) in the preliminary region in theimage 76 by extending the at least one of the one or more straight linefeatures to the third region.

After the make-up operation, the processing device 140 may determine theedge of the collimator based on the straight line features.

In some embodiments, the processing device 140 may obtain a physicalposition of the collimator in the imaging device. The processing device140 may project at least a part of the collimator on the breast imagebased on the physical position of the collimator. The processing device140 may determine the edge of the collimator in the breast image basedon the projection and the identified straight line features.

In some embodiments, the processing device 140 may crop the breast imagealong the determined edge of the collimator.

FIG. 38 is a flowchart illustrating an exemplary process for determiningan edge of a collimator of an imaging device in a breast image accordingto some embodiments of the present disclosure.

In some embodiments, the process 3800 may be implemented in the imagingsystem 100 illustrated in FIG. 1 . For example, the process 3800 may bestored in a storage medium (e.g., the storage device 150, or the storage220 of the processing device 140) as a form of instructions, and can beinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 of the processing device 140, or one or more modules inthe processing device 140 illustrated in FIG. 30 ). The operations ofthe illustrated process 3800 presented below are intended to beillustrative. In some embodiments, the process 3800 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 3800 as illustrated in FIG. 38 anddescribed below is not intended to be limiting.

In 3801, the processing device 140 may obtain a breast image andobtaining a binary template based on the breast image.

In 3802, the processing device 140 may perform first cropping to thebreast image and the binary template based on mechanical coordinates ofthe collimator.

In 3803, the processing device 140 may pre-process the breast image.

In 3804, the processing device 140 may extract a low gray template fromthe breast image.

In 3805, the processing device 140 may extract a high gray template fromthe breast image.

In 3806, the processing device 140 may obtain the first, second, third,and fourth sub-images by performing gradient transform and binarizationto the breast image.

In 3807, the processing device 140 may determine a preliminary regionand a center point of the preliminary region based on the binarytemplate and the first sub-image.

In 3808, the processing device 140 may add a make-up part to at leastone of one or more straight line based on the preliminary region.

In 3809, the processing device 140 may determine maximum projectionvalues in row and column directions in the second, third, and fourthsub-images.

In 3810, the processing device 140 may determine whether the maximumprojection values are larger than a preset threshold and whether thereis no direct exposure region on the side of the corresponding pixel rowor column away from the center point. If yes, the processing device 140may determine the pixel row or column corresponding to the maximumprojection value as the edge of the collimator region. If no, theprocessing device 140 may determine the edge corresponding to themechanical coordinates as the edge of the collimator region.

In 3816, the processing device 140 may determine whether the ratio ofthe high gray region to the direct exposure region is larger than apreset value. If yes, the processing device 140 may perform erosion tothe binary direct exposure region using a large erosion kernel. If no,the processing device 140 may perform erosion to the binary directexposure region using a small erosion kernel.

In 3815, the processing device 140 may output a template of thecollimator region to performing second cropping to the first-croppedbreast image.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

What is claimed is:
 1. A method for image enhancement implemented on amachine having at least one storage device and at least one processor,the method comprising: obtaining an original image; determining aplurality of decomposition coefficients of the original image bydecomposing the original image; determining at least one enhancementcoefficient by performing enhancement to at least one of the pluralityof decomposition coefficients using a coefficient enhancement model; andgenerating an enhanced image corresponding to the original image basedon the at least one enhancement coefficient.
 2. The method of claim 1,wherein the coefficient enhancement model is obtained by performingoperations including: obtaining a training set including a plurality ofsample pairs, each of the plurality of sample pairs including aplurality of first decomposition coefficients of a sample image and aplurality of second decomposition coefficients corresponding to thefirst decomposition coefficients; and obtaining the coefficientenhancement model by training, based on the training set, a preliminarymodel.
 3. The method of claim 2, wherein obtaining the training setincluding the plurality of sample pairs includes: obtaining an enhancedimage corresponding to the sample image by performing image processingon the sample image; obtaining the plurality of first decompositioncoefficients of the sample image and the plurality of seconddecomposition coefficients of the enhanced image corresponding to thesample image by decomposing the sample image and the correspondingenhanced image; and determining the plurality of first decompositioncoefficients and the plurality of second decomposition coefficients asthe sample pair.
 4. The method of claim 2, wherein obtaining thetraining set including the plurality of sample pairs includes: obtainingthe plurality of first decomposition coefficients of the sample image bydecomposing the sample image; obtaining the plurality of seconddecomposition coefficients by performing enhancement to the plurality offirst decomposition coefficients of the sample image; and determiningthe plurality of first decomposition coefficients of the sample imageand the plurality of second decomposition coefficients as the samplepair.
 5. The method of claim 4, wherein the image processing includes atleast one of a histogram equalization algorithm, a gamma conversionalgorithm, an exponential image enhancement algorithm, or a logarithmicimage enhancement algorithm.
 6. The method of claim 1, wherein thecoefficient enhancement model includes a trained deep learning model. 7.The method of claim 1, wherein the plurality of decompositioncoefficients of the original image is obtained by decomposing theoriginal image using a multi-resolution analysis algorithm.
 8. Themethod of claim 7, wherein the multi-resolution analysis algorithmincludes at least one of a Gauss-Laplace pyramid decomposition algorithmor a wavelet decomposition algorithm.
 9. The method of claim 1, furthercomprising: performing a pre-processing operation on the original image.10. A system for image enhancement, comprising: at least one storagedevice including a set of instructions; and at least one processor incommunication with the at least one storage device, wherein whenexecuting the set of instructions, the at least one processor isdirected to perform operations including: obtaining an original image;determining a plurality of decomposition coefficients of the originalimage by decomposing the original image; determining at least oneenhancement coefficient by performing enhancement to at least one of theplurality of decomposition coefficients using a coefficient enhancementmodel; and generating an enhanced image corresponding to the originalimage based on the at least one enhancement coefficient.
 11. The systemof claim 10, wherein the coefficient enhancement model is provided by:obtaining a training set including a plurality of sample pairs, each ofthe plurality of sample pairs including a plurality of firstdecomposition coefficients of a sample image and a plurality of seconddecomposition coefficients corresponding to the first decompositioncoefficients; and obtaining the coefficient enhancement model bytraining, based on the training set, a preliminary model.
 12. The systemof claim 11, wherein obtaining the training set including the pluralityof sample pairs includes: obtaining an enhanced image corresponding tothe sample image by performing image processing on the sample image;obtaining the plurality of first decomposition coefficients of thesample image and the plurality of second decomposition coefficients ofthe enhanced image corresponding to the sample image by decomposing thesample image and the corresponding enhanced image; and determining theplurality of first decomposition coefficients and the plurality ofsecond decomposition coefficients as the sample pair.
 13. The system ofclaim 12, wherein the image processing includes at least one of ahistogram equalization algorithm, a gamma conversion algorithm, anexponential image enhancement algorithm, or a logarithmic imageenhancement algorithm.
 14. The system of claim 11, wherein obtaining thetraining set including the plurality of sample pairs includes: obtainingthe plurality of first decomposition coefficients of the sample image bydecomposing the sample image; obtaining the plurality of seconddecomposition coefficients by performing enhancement to the plurality offirst decomposition coefficients of the sample image; and determiningthe plurality of first decomposition coefficients of the sample imageand the plurality of second decomposition coefficients as the samplepair.
 15. The system of claim 10, wherein the coefficient enhancementmodel includes a trained deep learning model.
 16. The system of claim10, wherein the plurality of decomposition coefficients of the originalimage is obtained by decomposing the original image using amulti-resolution analysis algorithm.
 17. The system of claim 16, whereinthe multi-resolution analysis algorithm includes at least one of aGauss-Laplace pyramid decomposition algorithm or a wavelet decompositionalgorithm.
 18. The system of claim 10, wherein the at least oneprocessor is directed to perform the operations including: performing apre-processing operation on the original image.
 19. The system of claim18, wherein performing the pre-processing operation on the originalimage includes: obtaining a pre-processed image by adjusting one or moredisplay parameters of the original image.
 20. A non-transitory computerreadable medium, comprising at least one set of instructions for imageenhancement, wherein when executed by one or more processors of acomputing device, the at least one set of instructions causes thecomputing device to perform a method, the method comprising: obtainingan original image; determining a plurality of decomposition coefficientsof the original image by decomposing the original image; determining atleast one enhancement coefficient by performing enhancement to at leastone of the plurality of decomposition coefficients using a coefficientenhancement model; and generating an enhanced image corresponding to theoriginal image based on the at least one enhancement coefficient.